Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

This thesis investigates logical formalizations of Castelfranchi and Falcone's (C&amp;F) theory of trust [9, 10, 11, 12]. The C&amp;F theory of trust defines trust as an essentially mental notion, making the theory particularly well suited for formalizations in multi-modal logics of beliefs, goals, intentions, actions, and time.Three different multi-modal logical formalisms intended for multi-agent systems are compared and evaluated along two lines of inquiry. First, I propose formal definitions of key concepts of the C&amp;F theory of trust and prove some important properties of these definitions. The proven properties are then compared to the informal characterisation of the C&amp;F theory. Second, the logics are used to formalize a case study involving an Internet forum, and their performances in the case study constitute grounds for a comparison. The comparison indicates that an accurate modelling of time, and the interaction of time and goals in particular, is integral for formal reasoning about trust.Finally, I propose a Horn fragment of the logic of Herzig, Lorini, Hubner, and Vercouter [25]. The Horn fragment is shown to be too restrictive to accurately express the considered case study.

This paper discusses an implementation of four speech acts: assert, concede, request and challenge in a paraconsistent framework. A natural four-valued model of interaction yields multiple new cognitive situations. They are analyzed in the context of communicative relations, which partially replace the concept of trust. These assumptions naturally lead to six types of situations, which often require performing conflict resolution and belief revision. The particular choice of a rule-based, DATALOC. like query language 4QL as a four-valued implementation framework ensures that, in contrast to the standard two-valued approaches, tractability of the model is achieved.

Answer Set Programming is a widely known knowledge representation framework based on the logic programming paradigm that has been extensively studied in the past decades. The semantic framework for Answer Set Programs is based on the use of stable model semantics. There are two characteristics intrinsically associated with the construction of stable models for answer set programs. Any member of an answer set is supported through facts and chains of rules and those members are in the answer set only if generated minimally in such a manner. These two characteristics, supportedness and minimality, provide the essence of stable models. Additionally, answer sets are implicitly partial and that partiality provides epistemic overtones to the interpretation of disjunctiver ules and default negation. This paper is intended to shed light on these characteristics by defining a semantic framework for answer set programming based on an extended first-order Kleene logic with weak and strong negation. Additionally, a definition of strongly supported models is introduced, separate from the minimality assumption explicit in stable models. This is used to both clarify and generate alternative semantic interpretations for answer set programs with disjunctive rules in addition to answer set programs with constraint rules. An algorithm is provided for computing supported models and comparative complexity results between strongly supported and stable model generation are provided.

In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Note: Accepted for publication.

Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

Various generalizations of fuzzy reasoning are frequently used in decision making. While in many application areas it is natural to assume that truth degrees of a property and its complement sum up to 1, such an assumption appears problematic, e.g., in modeling ignorance. Therefore, in some generalizations of fuzzy sets, degrees of membership in a set and in its complement are separated and are no longer required to sum up to 1. In frequent cases, this separation of positive and negative evidences for concept membership is more natural. As we discuss in the current paper, symbolic explanations of results of such forms of reasoning provide additional important information. In the present paper we address two related questions: (i) given generalized fuzzy connectives and a finite set of truth values T, find a finitely-valued logic over T, explaining fuzzy reasoning, and (ii) given a finitely-valued logic, find a fuzzy semantics, explained by the given logic. We also show examples illustrating usefulness of the approach.

This chapter presents a distributed architecture for unmanned aircraft systems that provides full integration of both low autonomy and high autonomy. The architecture has been instantiated and used in a rotorbased aerial vehicle, but is not limited to use in particular aircraft systems. Various generic functionalities essential to the integration of both low autonomy and high autonomy in a single system are isolated and described. The architecture has also been extended for use with multi-platform systems. The chapter covers the full spectrum of functionalities required for operation in missions requiring high autonomy. A control kernel is presented with diverse flight modes integrated with a navigation subsystem. Specific interfaces and languages are introduced which provide seamless transition between deliberative and reactive capability and reactive and control capability. Hierarchical Concurrent State Machines are introduced as a real-time mechanism for specifying and executing low-level reactive control. Task Specification Trees are introduced as both a declarative and procedural mechanism for specification of high-level tasks. Task planners and motion planners are described which are tightly integrated into the architecture. Generic middleware capability for specifying data and knowledge flow within the architecture based on a stream abstraction is also described. The use of temporal logic is prevalent and is used both as a specification language and as an integral part of an execution monitoring mechanism. Emphasis is placed on the robust integration and interaction between these diverse functionalities using a principled architectural framework. The architecture has been empirically tested in several complex missions, some of which are described in the chapter.

Skeletonisation is a key process in character recognition innatural images. Under the assumption that a character is made of astroke of uniform colour, with small variation in thickness, the processof recognising characters can be decomposed in the three steps. Firstthe image is segmented, then each segment is transformed into a set ofconnected strokes (skeletonisation), which are then abstracted in a descriptor that can be used to recognise the character. The main issue withskeletonisation is the sensitivity with noise, and especially, the presenceof holes in the masks. In this article, a new method for the extractionof strokes is presented, which address the problem of holes in the maskand does not use any parameters.

Characters recognition in natural images is a challenging problem, asit involves segmenting characters of various colours on various background. Inthis article, we present a method for segmenting images that use a colour percep-tion graph. Our algorithm is inspired by graph cut segmentation techniques andit use an edge detection technique for filtering the graph before the graph-cut aswell as merging segments as a final step. We also present both qualitative andquantitative results, which show that our algorithm perform at slightly better andfaster to a state of the art algorithm.

Learning to recognize and predict common activities, performed by objects and observed by sensors, is an important and challenging problem related both to artificial intelligence and robotics.In this thesis, the general problem of dynamic adaptive situation awareness is considered and we argue for the need for an on-line bottom-up approach.A candidate for a bottom layer is proposed, which we consider to be capable of future extensions that can bring us closer towards the goal.We present a novel approach to adaptive activity learning, where a mapping between raw data and primitive activity concepts are learned and continuously improved on-line and unsupervised. The approach takes streams of observations of objects as input and learns a probabilistic representation of both the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using probabilistic graphs.The learned model supports both estimating the most likely activity and predicting the most likely future (and past) activities. Methods and ideas from a wide range of previous work are combined to provide a uniform and efficient way to handle a variety of common problems related to learning, classifying and predicting activities.The framework is evaluated both by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an internally developed autonomous quadcopter system. The conclusion is that our framework is capable of learning the observed activities in real-time with good accuracy.We see this work as a step towards unsupervised learning of activities for robotic systems to adapt to new circumstances autonomously and to learn new activities on the fly that can be detected and predicted immediately.

We study a Horn fragment called Horn-RegI of the regular description logic with inverse RegI, which extends the description logic ALC with inverse roles and regular role inclusion axioms characterized by finite automata. In contrast to the well-known Horn fragmentsEL, DL-Lite, DLP, Horn-SH IQ and Horn-SROIQof description logics, Horn-RegI allows a form of the concept constructor universal restriction to appear at the left hand side of terminological inclusion axioms, while still has PTIME data complexity. Namely, a universal restriction can be used in such places in conjunction with the corresponding existential restriction. We provide an algorithm with PTIME data complexity for checking satisfiability of Horn-RegI knowledge bases.

We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.

We consider the directed Steiner tree problem (DSTP) with a constraint on the total number of arcs (hops) in the tree. This problem is known to be NP-hard, and therefore, only heuristics can be applied in the case of its large-scale instances.For the hop-constrained DSTP, we propose local search strategies aimed at improving any heuristically produced initial Steiner tree. They are based on solving a sequence of hop-constrained shortest path problems for which we have recently developed efficient label correcting algorithms.The presented approach is applied to finding suitable 3D locations where unmanned aerial vehicles (UAVs) can be placed to relay information gathered in multi-target monitoring and surveillance. The efficiency of our algorithms is illustrated by results of numerical experiments involving problem instances with up to 40 000 nodes and up to 20 million arcs.

This paper presents a comparison of two light-weight and low-cost airborne mapping systems. One is based on a lidar technology and the other on a video camera. The airborne lidar system consists of a high-precision global navigation satellite system (GNSS) receiver, a microelectromechanical system (MEMS) inertial measurement unit, a magnetic compass and a low-cost lidar scanner. The vision system is based on a consumer grade video camera. A commercial photogrammetric software package is used to process the acquired images and generate a digital surface model. The two systems are described and compared in terms of hardware requirements and data processing. The systems are also tested and compared with respect to their application on board of an unmanned aerial vehicle (UAV). An evaluation of the accuracy of the two systems is presented. Additionally, the multi echo capability of the lidar sensor is evaluated in a test site covered with dense vegetation. The lidar and the camera systems were mounted and tested on-board an industrial unmanned helicopter with maximum take-off weight of around 100 kilograms. The presented results are based on real flight-test data.

Reasoning about time and space is essential for many applications, especially for robots and other autonomous systems that act in the real world and need to reason about it. In this paper we present a pragmatic approach to spatio-temporal stream reasoning integrated in the Robot Operating System through the DyKnow framework. The temporal reasoning is done in the Metric Temporal Logic and the spatial reasoning in the Region Connection Calculus RCC-8. Progression is used to evaluate spatio-temporal formulas over incrementally available streams of states. To handle incomplete information the underlying first-order logic is extended to a three-valued logic. When incomplete spatial information is received, the algebraic closure of the known information is computed. Since the algebraic closure might have to be re-computed every time step, we separate the spatial variables into static and dynamic variables and reuse the algebraic closure of the static variables, which reduces the time to compute the full algebraic closure. The end result is an efficient and useful approach to spatio-temporal reasoning over streaming information with incomplete information.

The ability to automatically, on-demand, apply pattern matching over streams of information to infer the occurrence of events is an important fusion functionality. Existing event detection approaches require explicit configuration of what events to detect and what streams to use as input. This paper discusses on-demand semantic event processing, and extends the semantic information integration approach used in the stream processing middleware framework DyKnow to incorporate this new feature. By supporting on-demand semantic event processing, systems can automatically configure what events to detect and what streams to use as input for the event detection. This can also include the detection of lower-level events as well as processing of streams. The semantic stream query language C-SPARQL is used to specify events, which can be seen as transformations over streams. Since semantic streams consist of RDF triples, we suggest a method to convert between RDF streams and DyKnow streams. DyKnow is integrated in the Robot Operating System (ROS) and used for example in collaborative unmanned aircraft systems missions.

It is generally hard to predict the exact duration of an action. The uncertainty in the duration is often modeled in temporal planning by the use of upper bounds on durations, with the assumption that if an action happens to be executed more quickly, the plan will still succeed. However, this assumption is often false: If we finish cooking too early, the dinner will be cold before everyone is ready to eat. Simple Temporal Problems with Uncertainty (STPUs) allow us to model such situations. An STPU-based planner must verify that the plans it generates are executable, captured by the property of dynamic controllability. The EfficientIDC (EIDC) algorithm can do this incrementally during planning, with an amortized complexity per step of $O(n^3)$ but a worst-case complexity per step of $O(n^4)$. In this paper we show that the worst-case run-time of EIDC does occur, leading to repeated reprocessing of nodes in the STPU while verifying the dynamic controllability property. We present a new version of the algorithm, called EIDC2, which through optimal ordering of nodes avoids any need for reprocessing. This gives EIDC2 a strictly lower worst-case run-time, making it the fastest known algorithm for incrementally verifying dynamic controllability of STPUs.

We develop a new Web ontology rule language, called WORL, which combines a variant of OWL 2 RL with eDatalog Â¬ . We allow additional features like negation, the minimal number restriction and unary external checkable predicates to occur at the left-hand side of concept inclusion axioms. Some restrictions are adopted to guarantee a translation into eDatalog Â¬ . We also develop the well-founded semantics and the stable model semantics for WORL as well as the standard semantics for stratified WORL (SWORL) via translation into eDatalog Â¬ . Both WORL with respect to the well-founded semantics and SWORL with respect to the standard semantics have PTime data complexity. In contrast to the existing combined formalisms, in WORL and SWORL negation in concept inclusion axioms is interpreted using nonmonotonic semantics.

In this report we compare three different assignment algorithms in how they can be used to assign a set of drones to get to a set of goal locations in an as resource efficient way as possible. An experiment is set up to compare how these algorithms perform in a somewhat realistic simulated environment. The Robot Operating system (ROS) is used to create the experimental environment. We found that by introducing a threshold for the Hungarian algorithm we could reduce the total time it takes to complete the problem while only sightly increasing total distance traversed by the drones.

In Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS), pages 199–207. ISBN:ISBN:978-1-57735-660-8.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are uncontrollable (determined by nature), as is often the case for actions in planning. It is essential to verify that such networks are dynamically controllable (DC) â executable regardless of the outcomes of uncontrollable durations â and to convert them to an executable form. We use insights from incremental DC verification algorithms to re-analyze the original verification algorithm. This algorithm, thought to be pseudo-polynomial and subsumed by an O(n5) algorithm and later an O(n4) algorithm, is in fact O(n4) given a small modification. This makes the algorithm attractive once again, given its basis in a less complex and more intuitive theory. Finally, we discuss a change reducing the amount of work performed by the algorithm.

In Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART), pages 130–141. DOI:10.5220/0004815801300141.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems wheresome durations are uncontrollable (determined by nature), as is often the case for actions in planning. It is essentialto verify that such networks are dynamically controllable (DC) â executable regardless of the outcomesof uncontrollable durations â and to convert them to an executable form. We use insights from incrementalDC verification algorithms to re-analyze the original verification algorithm. This algorithm, thought to bepseudo-polynomial and subsumed by an O(n<sup>5</sup>) algorithm and later an O(n<sup>4</sup>) algorithm, is in fact O(n<sup>4</sup>) givena small modification. This makes the algorithm attractive once again, given its basis in a less complex andmore intuitive theory. Finally, we discuss a change reducing the amount of work performed by the algorithm.

After natural disasters such as earthquakes, floods, hurricanes, tornados and fires, providing emergency management schemes which mainly rely on communications systems is essential for rescue operations. To establish an emergency communications system during unforeseen events such as natural disasters, we propose the use of a team of unmanned aerial vehicles (UAVs). The proposed system is a post-disaster solution and can be used whenever and wherever required. Each UAV in the team has an onboard computer which runs three main subsystems responsible for end-to-end communication, formation control and autonomous navigation. The onboard computer and the low-level controller of the UAV cooperate to accomplish the objective of providing local communications infrastructure. In this study, the subsystems running on each UAV are explained and evaluated by simulation studies and field tests using an autonomous helicopter. While the simulation studies address the efficiency of the end-to-end communication subsystem, the field tests evaluate the accuracy of the navigation subsystem. The results of the field tests and the simulation studies show that the proposed system can be successfully used in case of disasters to establish an emergency communications system.

We introduce a Horn description logic called Horn-DL, which is strictly and essentially richer than Horn- SROIQ , while still has PTime data complexity. In comparison with Horn- SROIQ , HornDL additionally allows the universal role and assertions of the form irreflexive <em>(s)</em>, Â¬s(a,b) , aâÌ¸b . More importantly, in contrast to all the well-known Horn fragments EL , DL-Lite, DLP, Horn- SHIQ , Horn- SROIQ of description logics, HornDL allows a form of the concept constructor âuniversal restrictionâ to appear at the left hand side of terminological inclusion axioms. Namely, a universal restriction can be used in such places in conjunction with the corresponding existential restriction. In the long version of this paper, we present the first algorithm with PTime data complexity for checking satisfiability of HornDL knowledge bases.

The logic TeamLog proposed by Dunin-KÄplicz and Verbrugge is used to express properties of agentsâ cooperation in terms of individual, bilateral and collective informational and motivational attitudes like beliefs, goals and intentions. In this paper we isolate a Horn fragment of TeamLog, called Horn-TeamLog, and we show that it has PTime data complexity.

We study Horn fragments of serial multimodal logics which are characterized by regular grammars with converse. Such logics are useful for reasoning about epistemic states of multiagent systems as well as similarity-based approximate reasoning. We provide the first algorithm with PTIME data complexity for checking satisfiability of a Horn knowledge base in a serial regular grammar logic with converse.

Agents' beliefs can be incomplete and partially inconsistent. The process of agents' belief formation in such contexts has to be supported by suitable tools allowing one to express a variety of inconsistency resolving and nonmonotonic reasoning techniques.In this paper we discuss 4QL*, a general purpose rule-based query language allowing one to use rules with negation in the premises and in the conclusions of rules. It is based on a simple and intuitive semantics and provides uniform tools for lightweight versions of well-known forms of nonmonotonic reasoning. In addition, it is tractable w.r.t. data complexity and captures PTIME queries, so can be used in real-world applications.Reasoning in 4QL* is based on well-supported models. We simplify and at the same time generalize previous definitions of well-supported models and develop a new algorithm for computing such models.

This paper discusses an implementation of four speech acts: assert, concede, request and challenge in a paraconsistent framework. A natural four-valued model of interaction yields multiple new cognitive situations. They are analyzed in the context of communicative relations, which partially replace the concept of trust. These assumptions naturally lead to six types of situations, which often require performing conflict resolution and belief revision.The particular choice of a rule-based, DATALOG$^{\neg \neg}$-like query language 4QL as a four-valued implementation framework ensures that, in contrast to the standard two-valued approaches, tractability of the model is achieved.

A novel formalization of beliefs in multiagent systems has recently been proposed by Dunin-KÄplicz and SzaÅas. The aim has been to bridge the gap between idealized logical approaches to modeling beliefs and their actual implementations. Therefore the stages of belief acquisition, intermediate reasoning and final belief formation have been isolated and analyzed. In conclusion, a novel semantics reflecting those stages has been provided. This semantics is based on the new concept of epistemic profile, reflecting agentâs reasoning capabilities in a dynamic and unpredictable environment. The presented approach appears suitable for building complex belief structures in the context of incomplete and/or inconsistent information. One of original ideas is that of epistemic profiles serving as a tool for transforming preliminary beliefs into final ones. As epistemic profile can be devised both on an individual and a group level in analogical manner, a uniform treatment of single agent and group beliefs has been achieved.In the current paper these concepts are further elaborated. Importantly, we indicate an implementation framework ensuring tractability of reasoning about beliefs, propose the underlying methodology and illustrate it on an example.

This paper focuses on approximate reasoning based on the use of approximation spaces. Approximation spaces and the approximated relations induced by them are a generalization of the rough set-based approximations of Pawlak. Approximation spaces are used to define neighborhoods around individuals and rough inclusion functions. These in turn are used to define approximate sets and relations. In any of the approaches, one would like to embed such relations in an appropriate logical theory which can be used as a reasoning engine for specific applications with specific constraints. We propose a framework which permits a formal study of the relationship between properties of approximations and properties of approximation spaces. Using ideas from correspondence theory, we develop an analogous framework for approximation spaces. We also show that this framework can be strongly supported by automated techniques for quantifier elimination.

The IEEE Safety, Security, and Rescue Robotics community has created a roadmap for producing unmanned systems that could be adopted by the Public Safety sector within 10 years, given appropriate R&amp;D investment especially in human-robot interaction and perception. The five applications expected to be of highest value to the Public Safety community, highest value first, are: assisting with routine inspection of the critical infrastructure, âchronic emergenciesâ such as firefighting, hazardous material spills, port inspection, and damage estimation after a disaster. The technical feasibility of the applications were ranked, with the most attractive scenario, infrastructure inspection, rated as the second easiest scenario; this suggests the maturity of robotics technology is beginning to match stakeholder needs. Each of the five applications were discussed in terms of the six broad enabling technology areas specified in the current National Robotics Initiative Roadmap (perception, human-robot interaction, mechanisms, modeling and simulation, control and planning, and testing and evaluation) and nine specific capabilities identified by the community as being essential to commercialization (communication, alerting, localization, fault tolerance, mapping, manpower needs, plug and play capabilities, multiple users, and multiple robots). The community believes that perception and human-robot interaction are the two biggest barriers to adoption, and require more research, given that their low technical maturity (3rd and 6th rank respectively). However, each of the specific capabilities needed for commercialization are being addressed by current research and could be achieved within 10 years with sustained funding.

When developing software that is meant to be distributed over several different computers and several different networks while still working together against a common goal there is a challenge in testing how updates within a single component will affect the system as a whole. Even if the performance of that specific component increases that is no guarantee for the increased performance of the entire system. Traditional methods of testing software becomes both hard and tedious when several different machines has to be involved for a single test and all of those machines has to be synchronized as well.This thesis has resulted in an exemplary application suite for testing distributed software. The thesis describes the method used for implementation as well as a description of the actual application suite that was developed. During the development several important factors and improvements for such a system was identified, which are described at the end of the thesis even though some of them never made it into the actual implementation. The implemented application suite could be used as a base when developing a more complete system in order to distribute tests and applications that has to run in a synchronized manner with the ability to report the results of each individual component.

Searching with a sensor for objects and to observe parts of a known environment efficiently is a fundamental prob- lem in many real-world robotic applications such as household robots searching for objects, inspection robots searching for leaking pipelines, and rescue robots searching for survivors after a disaster. We consider the problem of identifying and planning efficient view point sequences for covering complex 3d environments. We compare empirically several variants of our algorithm that allow to trade-off schedule computation against execution time. Our results demonstrate that, despite the intractability of the overall problem, computing effective solutions for coverage search in real 3d environments is feasible.

The current article is devoted to extensions of the rule query language 4QL proposed by MaÅuszyÅski and SzaÅas. 4QL is a Datalog<sup>Â¬Â¬</sup>-like language, allowing one to use rules with negation in heads and bodies of rules. It is based on a simple and intuitive semantics and provides uniform tools for lightweight versions of well-known forms of non-monotonic reasoning. In addition, 4QL is tractable w.r.t. data complexity and captures PTime queries. In the current article we relax most of restrictions of 4QL, obtaining a powerful but still tractable query language 4QL<sup>+</sup>. In its development we mainly focused on its pragmatic aspects: simplicity, tractability and generality. In the article we discuss our approach and choices made, define a new, more general semantics and investigate properties of 4QL<sup>+</sup>.

In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5935–5942. IEEE conference proceedings. ISBN:978-146736358-7.DOI:10.1109/IROS.2013.6697217.

High level reasoning is becoming essential to autonomous systems such as robots. Both the information available to and the reasoning required for such autonomous systems is fundamentally incremental in nature. A stream is a flow of incrementally available information and reasoning over streams is called stream reasoning. Incremental reasoning over streaming information is necessary to support a number of important robotics functionalities such as situation awareness, execution monitoring, and decision making.This paper presents a practical framework for semantically grounded temporal stream reasoning called DyKnow. Incremental reasoning over streams is achieved through efficient progression of temporal logical formulas. The reasoning is semantically grounded through a common ontology and a specification of the semantic content of streams relative to the ontology. This allows the finding of relevant streams through semantic matching. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning framework is integrated in the Robot Operating System (ROS) thereby extending it with a stream reasoning capability.

In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2.

The paper presents a light-weight and low-cost airborne terrain mapping system. The developed Airborne LiDAR Scanner (ALS) sys- tem consists of a high-precision GNSS receiver, an inertial measurement unit and a magnetic compass which are used to complement a LiDAR sensor in order to compute the terrain model. Evaluation of the accuracy of the generated 3D model is presented. Additionally, a comparison is provided between the terrain model generated from the developed ALS system and a model generated using a commer- cial photogrammetric software. Finally, the multi-echo capability of the used LiDAR sensor is evaluated in areas covered with dense vegetation. The ALS system and camera systems were mounted on-board an industrial unmanned helicopter of around 100 kilograms maximum take-off weight. Presented results are based on real flight-test data.

In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pages 6013–6018. DOI:10.1109/IROS.2013.6697229.

In this paper we consider the problem of searching for an arbitrarily smart and fast evader in a large environment with a team of unmanned aerial vehicles (UAVs) while providing guarantees of detection. Our emphasis is on the fast execution of efficient search strategies that minimize the number of UAVs and the search time. We present the first approach for computing fast search strategies utilizing additional searchers to speed up the execution time and thereby enabling large scale UAV search. In order to scale to very large environments when using UAVs one would either have to overcome the energy limitations of UAVs or pay the cost of utilizing additional UAVs to speed up the search. Our approach is based on coordinating UAVs on sweep lines, covered by the UAV sensors, that move simultaneously through an environment. We present some simulation results that show a significant reduction in execution time when using multiple UAVs and a demonstration of a real system with three ARDrones.

In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pages 1648–1655. DOI:10.1109/IROS.2013.6696570.

Efficient task allocation with timing constraints to a team of possibly heterogeneous robots is a challenging problem with application, e.g., in search and rescue. In this paper a mixed-integer linear programming (MILP) approach is proposed for assigning heterogeneous robot teams to the simultaneous completion of sequences of tasks with specific requirements such as completion deadlines. For this purpose our approach efficiently combines the strength of state of the art Mixed Integer Linear Programming (MILP) solvers with human expertise in mission scheduling. We experimentally show that simple and intuitive inputs by a human user have substantial impact on both computation time and quality of the solution. The presented approach can in principle be applied to quite general missions for robot teams with human supervision.

Air combat is a complex situation, training for it and analysis of possible tactics are time consuming and expensive. In order to circumvent those problems, mathematical models of air combat can be used. This thesis presents air combat as a one-on-one influence diagram game where the influence diagram allows the dynamics of the aircraft, the preferences of the pilots and the uncertainty of decision making in a structural and transparent way to be taken into account. To obtain the playersâ game optimal control sequence with respect to their preferences, the influence diagram has to be solved. This is done by truncating the diagram with a moving horizon technique and determining and implementing the optimal controls for a dynamic game which only lasts a few time steps.The result is a working air combat model, where a player estimates the probability that it resides in any of four possible states. The pilotâs preferences are modeled by utility functions, one for each possible state. In each time step, the players are maximizing the cumulative sum of the utilities for each state which each possible action gives. These are weighted with the corresponding probabilities. The model is demonstrated and evaluated in a few interesting aspects. The presented model offers a way of analyzing air combat tactics and maneuvering as well as a way of making autonomous decisions in for example air combat simulators.

Communication through text messaging, SMS (Short Message Service), is nowadays a huge industry with billions of active users. Because of the huge userbase it has attracted many companies trying to market themselves through unsolicited messages in this medium in the same way as was previously done through email. This is such a common phenomenon that SMS spam has now become a plague in many countries.This report evaluates several established machine learning algorithms to see how well they can be applied to the problem of filtering unsolicited SMS messages. Each filter is mainly evaluated by analyzing the accuracy of the filters on stored message data. The report also discusses and compares requirements for hardware versus performance measured by how many messages that can be evaluated in a fixed amount of time.The results from the evaluation shows that a decision tree filter is the best choice of the filters evaluated. It has the highest accuracy as well as a high enough process rate of messages to be applicable. The decision tree filter which was found to be the most suitable for the task in this environment has been implemented. The accuracy in this new implementation is shown to be as high as the implementation used for the evaluation of this filter.Though the decision tree filter is shown to be the best choice of the filters evaluated it turned out the accuracy is not high enough to meet the specified requirements. It however shows promising results for further testing in this area by using improved methods on the best performing algorithms.

In 16th International Conference on Information Fusion, pages 445–452. ISBN:978-605-86311-1-3.

The automatic, on-demand, integration of informationfrom multiple diverse sources outside the control of theapplication itself is central to many fusion applications. Animportant problem is to handle situations when the requestedinformation is not directly available but has to be generatedor adapted through transformations. This paper extends thesemantic information integration approach used in the streambasedknowledge processing middleware DyKnow with supportfor finding and automatically applying transformations. Twotypes of transformations are considered. Automatic transformationbetween different units of measurements and betweenstreams of different types. DyKnow achieves semantic integrationby creating a common ontology, specifying the semantic contentof streams relative to the ontology and using semantic matchingto find relevant streams. By using semantic mappings betweenontologies it is also possible to do semantic matching overmultiple ontologies. The complete stream reasoning approach isintegrated in the Robot Operating System (ROS) and used incollaborative unmanned aircraft systems missions.

We study the complexity of finding so called spanner paths between arbitrary nodes in Euclidean graphs. We study both general Euclidean graphs and a special type of graphs called Integer Graphs. The problem is proven NP-complete for general Euclidean graphs with non-constant stretches (e.g. (2n)^(3/2) where n denotes the number of nodes in the graph). An algorithm solving the problem in O(2^(0.822n)) is presented. Integer graphs are simpler and for these special cases a better algorithm is presented. By using a partial order of so called Images the algorithm solves the spanner path problem using O(2^(c(\log n)^2)) time, where c is a constant depending only on the stretch.

Rich geometric models of the environment are needed for robots to carry out their missions. However a robot operating in a large environment would require a compact representation. In this article, we present a method that relies on the idea that a plane appears as a line segment in a 2D scan, and that by tracking those lines frame after frame, it is possible to estimate the parameters of that plane. The method is divided in three steps: fitting line segments on the points of the 2D scan, tracking those line segments in consecutive scan and estimating the parameters with a graph based SLAM (Simultaneous Localisation And Mapping) algorithm.

Automated specification, generation and execution of high level missions involving one or more heterogeneous unmanned aircraft systems is in its infancy. Much previous effort has been focused on the development of air vehicle platforms themselves together with the avionics and sensor subsystems that implement basic navigational skills. In order to increase the degree of autonomy in such systems so they can successfully participate in more complex mission scenarios such as those considered in emergency rescue that also include ongoing interactions with human operators, new architectural components and functionalities will be required to aid not only human operators in mission planning, but also the unmanned aircraft systems themselves in the automatic generation, execution and partial verification of mission plans to achieve mission goals. This article proposes a formal framework and architecture based on the unifying concept of delegation that can be used for the automated specification, generation and execution of high-level collaborative missions involving one or more air vehicles platforms and human operators. We describe an agent-based software architecture, a temporal logic based mission specification language, a distributed temporal planner and a task specification language that when integrated provide a basis for the generation, instantiation and execution of complex collaborative missions on heterogeneous air vehicle systems. A prototype of the framework is operational in a number of autonomous unmanned aircraft systems developed in our research lab.

Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring.In this paper we present a stream-based hierarchical anchoring framework. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.

When parts of the states in a goal POMDP are fully observable and some actions are deterministic it is possibleto take advantage of these properties to efficiently generate approximate solutions. Actions that deterministically affect the fully observable component of the world state can be abstracted away and combined into macro actions, permitting a planner to converge more quickly. This processing can be separated from the main search procedure, allowing us to leverage existing POMDP solvers. Theoretical results show how a POMDP can be analyzed to identify the exploitable properties and formal guarantees are provided showing that the use of macro actions preserves solvability. The efficiency of the method is demonstrated with examples when used in combination with existing POMDP solvers.

In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press. ISBN:978-1-57735-609-7.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are determined by nature, as is often the case for actions in planning. As such networks are generated it is essential to verify that they are dynamically controllable â executable regardless of the outcomes of uncontrollable durations â and to convert them to a dispatchable form. The previously published FastIDC algorithm achieves this incrementally and can therefore be used efficiently during plan construction. In this paper we show that FastIDC is not sound when new constraints are added, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm, pinpoint the cause, and show how the algorithm can be modified to correctly detect uncontrollable networks.

We consider the problem of detecting all moving and evading targets in 2.5D environments with teams of UAVs. Targets are assumed to be fast and omniscient while UAVs are only equipped with limited range detection sensors and have no prior knowledge about the location of targets. We present an algorithm that, given an elevation map of the environment, computes synchronized trajectories for the UAVs to guarantee the detection of all targets. The approach is based on coordinating the motion of multiple UAVs on sweep lines to clear the environment from contamination, which represents the possibility of an undetected target being located in an area. The goal is to compute trajectories that minimize the number of UAVs needed to execute the guaranteed search. This is achieved by converting 2D strategies, computed for a polygonal representation of the environment, to 2.5D strategies. We present methods for this conversion and consider cost of motion and visibility constraints. Experimental results demonstrate feasibility and scalability of the approach. Experiments are carried out on real and artificial elevation maps and provide the basis for future deployments of large teams of real UAVs for guaranteed search.

We propose RMASBench, a new benchmarking tool based on the RoboCup Rescue Agent simulation system, to easily compare coordination approaches in a dynamic rescue scenario. In particular, we offer simple interfaces to plug-in coordination algorithms without the need for implementing and tuning low-level agents behaviors. Moreover, we add to the realism of the simulation by providing a large scale crowd simulator, which exploits GPUs parallel architecture, to simulate the behavior of thousands of agents in real time. Finally, we focus on a specific coordination problem where fire fighters must combat fires and prevent them from spreading across the city. We formalize this problem as a Distributed Constraint Optimization Problem and we compare two state-of-the art solution techniques: DSA and MaxSum. We perform an extensive empirical evaluation of such techniques considering several standard measures for performance (e.g. damages to buildings) and coordination overhead (e.g., message exchanged and non concurrent constraint checks). Our results provide interesting insights on limitations and benefits of DSA and MaxSum in our rescue scenario and demonstrate that RMASBench offers powerful tools to compare coordination algorithms in a dynamic environment.

The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (e.g. Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.

In Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA), pages 2977–2983. In series: Robotics and Automation (ICRA), 2013 IEEE International Conference on #??. IEEE conference proceedings. ISBN:978-1-4673-5641-1.DOI:10.1109/ICRA.2013.6630990.

We consider the problem of detecting moving and evading targets by a team of coordinated unmanned aerial vehicles (UAVs) in large and complex 2D and 2.5D environments. Our approach is based on the coordination of 2D sweep lines that move through the environment to clear it from all contamination, representing the possibility of a target being located in an area, and thereby detecting all targets. The trajectories of the UAVs are implicitly given by the motion of these sweep lines and their costs are determined by the number of UAVs needed. A novel algorithm that computes low cost coordination strategies of the UAV sweep lines in simply connected polygonal environments is presented. The resulting strategies are then converted to strategies clearing multiply connected and 2.5D environments. Experiments on real and artificial elevation maps with complex visibility constraints are presented and demonstrate the feasibility and scalability of the approach. The algorithms used for the experiments are made available on a public repository.

This paper presents presents a study on eciency of Urban Search and Rescue (USAR) missions that has been carried out within the framework of the German research project I-LOV. After three years of development, first field tests have been carried out in 2011 by professionals such as the Rapid Deployment Unit for Salvage Operations Abroad (SEEBA). We present results from evaluating search teams in simulated USAR scenarios equipped with newly developed technical search means and digital data input terminals developed in the I-LOV project. In particular, USAR missions assisted by the âbioradarâ, a ground-penetrating radar system for the detection of humanoid movements, a semi-active video probe of more than 10 m length for rubble pile exploration, a snake-like rescue robot, and the decision support system FRIEDAA were evaluated and compared with conventional USAR missions. Results of this evaluation indicate that the developed technologies represent an advantages for USAR missions, which are discussed in this paper.

This book is dedicated to the memory of Professor Zdzis{\l}aw Pawlak who passed away almost six year ago. He is the founder of the Polish school of Artificial Intelligence and one of the pioneers in Computer Engineering and Computer Science with worldwide influence. He was a truly great scientist, researcher, teacher and a human being.This book prepared in two volumes contains more than 50 chapters. This demonstrates that the scientific approaches discovered by of Professor Zdzis{\l}aw Pawlak, especially the rough set approach as a tool for dealing with imperfect knowledge, are vivid and intensively explored by many researchers in many places throughout the world. The submitted papers prove that interest in rough set research is growing and is possible to see many new excellent results both on theoretical foundations and applications of rough sets alone or in combination with other approaches.We are proud to offer the readers this book.

The current paper is devoted to belief fusion when information sources may deliver incomplete and inconsistent information. In such cases paraconsistent and commonsense reasoning techniques can be used to complete missing knowledge and disambiguate inconsistencies. We propose a novel, realistic model of distributed belief fusion and an implementation framework guaranteeing its tractability.

We consider the problem of an autonomous robot searching for objects in unknown 3d space. Similar to the well known frontier-based exploration in 2d, the problem is to determine a minimal sequence of sensor viewpoints until the entire search space has been explored. We introduce a novel approach that combines the two concepts of voids, which are unexplored volumes in 3d, and frontiers, which are regions on the boundary between voids and explored space. Our approach has been evaluated on a mobile platform equipped with a manipulator searching for victims in a simulated USAR setup. First results indicate the real-world capability and search efficiency of the proposed method.

Research with collaborative robotic systems has much to gain by leveraging concepts and ideas from the areas of multi-agent systems and the social sciences. In this paper we propose an approach to formalizing and grounding important aspects of collaboration in a collaborative system shell for robotic systems. This is done primarily in terms of the concept of delegation, where delegation will be instantiated as a speech act. The formal characterization of the delegation speech act is based on a preformal theory of delegation proposed by Falcone and Castelfranchi. We show how the delegation speech act can in fact be used to formally ground an abstract characterization of delegation into a FIPA-compliant implementation in an agent-oriented language such as JADE, as part of a collaborative system shell for robotic systems. The collaborative system shell has been developed as a prototype and used in collaborative missions with multiple unmanned aerial vehicle systems.

In the next decades, practically viable robotic/agent systems are going to be mixed-initiative in nature. Humans will request help from such systems and such systems will request help from humans in achieving the complex mission tasks required. Pragmatically, one requires a distributed task specification language to define tasks and a suitable data structure which satisfies the specification and can be used flexibly by collaborative multi-agent/robotic systems. This paper defines such a task specification language and an abstract data structure called Task Specification Trees which has many of the requisite properties required for mixed-initiative problem solving and adjustable autonomy in a distributed context. A prototype system has been implemented for this delegation framework and has been used practically with collaborative unmanned aircraft systems.

Unmanned aircraft systems (UASs) are now becoming technologically mature enough to be integrated into civil society. An essential issue is principled mixed-initiative interaction between UASs and human operators. Two central problems are to specify the structure and requirements of complex tasks and to assign platforms to these tasks. We have previously proposed Task Specification Trees (TSTs) as a highly expressive specification language for complex multi-agent tasks that supports mixed-initiative delegation and adjustable autonomy. The main contribution of this paper is a sound and complete distributed heuristic search algorithm for allocating the individual tasks in a TST to platforms. The allocation also instantiates the parameters of the tasks such that all the constraints of the TST are satisfied. Constraints are used to model dependencies between tasks, resource usage as well as temporal and spatial requirements on complex tasks. Finally, we discuss a concrete case study with a team of unmanned aerial vehicles assisting in a challenging emergency situation.

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 1270–1277. IEEE. DOI:10.1109/ICRA.2012.6225060.

We present a general framework to estimate the parameters of both a robot and landmarks in 3D. It relies on the use of a stochastic gradient descent method for the optimisation of the nodes in a graph of weak constraints where the landmarks and robot poses are the nodes. Then a belief propagation method combined with covariance intersection is used to estimate the uncertainties of the nodes. The first part of the article describes what is needed to define a constraint and a node models, how those models are used to update the parameters and the uncertainties of the nodes. The second part present the models used for robot poses and interest points, as well as simulation results.

One challenging problem in disaster response is to efficiently assign resources such as fire fighters and trucks to local incidents that are spatially distributed on a map. Existing systems for command and control (C2/C4I) are coming with powerful interfaces enabling the manual assignment of resources to the incident commander. However, with increasing number of local incidents over time the performance of manual methods departs arbitrarily from an optimal solution. In this paper we introduce preliminary results of building an interface between existing professional C2/C4I systems and Constraint Satisfaction Problem (CSP)-solvers. We show by using an example the feasibility of scheduling and assigning missions having deadlines and resource constraints.

The goal of the paper is to present the foreseen research activity of the European project âSHERPAâ whose activities will start officially on February 1th 2013. The goal of SHERPA is to develop a mixed ground and aerial robotic platform to support search and rescue activities in a real-world hostile environment, like the alpine scenario that is specifically targeted in the project. Looking into the technological platform and the alpine rescuing scenario, we plan to address a number of research topics about cognition and control. What makes the project potentially very rich from a scientific viewpoint is the heterogeneity and the capabilities to be owned by the different actors of the SHERPA system: the human rescuer is the âbusy geniusâ, working in team with the ground vehicle, as the âintelligent donkeyâ, and with the aerial platforms, i.e. the âtrained waspsâ and âpatrolling hawksâ. Indeed, the research activity focuses on how the âbusy geniusâ and the âSHERPA animalsâ interact and collaborate with each other, with their own features and capabilities, toward the achievement of a common goal.

Use of Unmanned Aerial Vehicles have seen enormous growth in recent years due to the advances in related scientific and technological fields. This fact combined with decreasing costs of using UAVs enables their use in new application areas. Many of these areas are suitable for miniature scale UAVs - Micro Air Vehicles(MAV) - which have the added advantage of portability and ease of deployment. One of the main functionalities necessary for successful MAV deployment in real-world applications is autonomous landing. Landing puts particularly high requirements on positioning accuracy, especially in indoor confined environments where the common global positioning technology is unavailable. For that reason using an additional sensor, such as a camera, is beneficial. In this thesis, a set of technologies for achieving autonomous landing is developed and evaluated. In particular, state estimation based on monocular vision SLAM techniques is fused with data from onboard sensors. This is then used as the basis for nonlinear adaptive control as well trajectory generation for a simple landing procedure. These components are connected using a new proposed framework for robotic development. The proposed system has been fully implemented and tested in a simulated environment and validated using recorded data. Basic autonomous landing was performed in simulation and the result suggests that the proposed system is a viable solution for achieving a fully autonomous landing of a quadrotor.

In Proceedings of the International Workshop on Perception for Mobile Robots Autonomy (PEMRA).

Rich geometric models of the environment are needed for robots to accomplish their missions. However a robot operatingin a large environment would require a compact representation.In this article, we present a method that relies on the idea that a plane appears as a line segment in a 2D scan, andthat by tracking those lines frame after frame, it is possible to estimate the parameters of that plane. The method istherefore divided in three steps: fitting line segments on the points of the 2D scan, tracking those line segments inconsecutive scan and estimating the parameters with a graph based SLAM (Simultaneous Localisation And Mapping)algorithm.

We develop the first bisimulation-based method of concept learning, called BBCL, for knowledge bases in description logics (DLs). Our method is formulated for a large class of useful DLs, with well-known DLs like <em>ALC, SHIQ, SHOIQ, SROIQ</em>. As bisimulation is the notion for characterizing indis-cernibility of objects in DLs, our method is natural and very promising.

We claim that competitive elements can improve thequality of programming and algorithms courses. To test this, weused our experience from organising national and internationalprogramming competitions to design and evaluate two differentcontests in an introductory algorithms course. The first contestturned lab assignments into a competition, where two groups rancompetitions and two were control groups and did not compete.The second, voluntary, contest, consisting of 15 internationalprogramming competition style problems, was designed tosupport student skill acquisition by providing them withopportunities for deliberate practise. We found that competitiveelements do influence student behaviour and our mainconclusions from the experiment are that students really likecompetitions, that the competition design is very important forthe resulting behaviour of the students, and that active studentsperform better on exams.We also report on an extra-curricular activity in the form of asemester long programming competition as a way of supportingstudent's deliberate practise in computer programming.

This book constitutes the refereed proceedings of the 6th KES International Conference on Agent and Multi-Agent Systems, KES-AMSTA 2012, held in Dubrovnik, Croatia, in June 2012. &lt;br&gt;The conference attracted a substantial number of researchers and practitioners from all over the world who submitted their papers for ten main tracks covering the methodology and applications of agent and multi-agent systems, one workshop (TRUMAS 2012) and five special sessions on specific topics within the field. The 66 revised papers presented were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on virtual organizations, knowledge and learning agents, intelligent workflow, cloud computing and intelligent systems, self-organization, ICT-based alternative and augmentative communication, multi-agent systems, mental and holonic models, assessment methodologies in multi-agent and other paradigms, business processing agents, Trumas 2012 (first international workshop), conversational agents and agent teams, digital economy, and multi-agent systems in distributed environments.

The LNCS journal Transactions on Computational Collective Intelligence (TCCI) focuses on all facets of computational collective intelligence (CCI) and their applications in a wide range of fields such as the Semantic Web, social networks and multi-agent systems. TCCI strives to cover new methodological, theoretical and practical aspects of CCI understood as the form of intelligence that emerges from the collaboration and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc., aims to support human and other collective intelligence and to create new forms of CCI in natural and/or artificial systems.This, the sixth issue of Transactions on Computational Collective Intelligence contains 10 selected papers, focusing on the topics of classification, agent cooperation, paraconsistent reasoning and agent distributed mobile interaction.

Streams of information rather than static databases are becoming increasingly important with the rapid changes involved in a number of fields such as finance, social media and robotics. DyKnow is a stream-based knowledge processing middleware which has been used in autonomous Unmanned Aerial Vehicle (UAV) research. ROS (Robot Operating System) is an open-source robotics framework providing hardware abstraction, device drivers, communication infrastructure, tools, libraries as well as other functionalities.This thesis describes a design and a realization of stream processing in ROS based on the stream-based knowledge processing middleware DyKnow. It describes how relevant information in ROS can be selected, labeled, merged and synchronized to provide streams of states. There are a lot of applications for such stream processing such as execution monitoring or evaluating metric temporal logic formulas through progression over state sequences containing the features of the formulas. Overviews are given of DyKnow and ROS before comparing the two and describing the design. The stream processing capabilities implemented in ROS are demonstrated through performance evaluations which show that such stream processing is fast and efficient. The resulting realization in ROS is also readily extensible to provide further stream processing functionality.

An Unmanned Aerial Vehicle (UAV) is often an aircraft with no crew that can fly independently by a preprogrammed plan, or by remote control. Several UAV applications, like autonomously surveillance and traffic monitoring, are real-time applications. Hence tasks in these applications must complete within specied deadlines.Real Time Calculus (RTC) is a formal framework for reasoning about realtime systems and in particular streaming applications. RTC has its mathematical roots in Network Calculus. It supports timing analysis, estimating loads and predicting memory requirements.In this thesis, a formal analysis of real-time stream reasoning for UAV applications is conducted. The performance analysis is based on RTC using an abstract performance model of the streaming reasoning on board a UAV. In this study, we consider two dierent scheduling methods, first-in-first-out (FIFO) and fixed priority (FP). In the FIFO scheduling model the priorities of the tasks are assigned and processed based on the order of their arrival, while in the FP scheduling model the priorities of the tasks are preassigned. The Quality of Service (QoS) of these applications is calculated and analyzed in a proposed design space exploration framework.QoS can be defined dierently depending on what field we are studying and in this thesis we are interested in studying the delays of the real-time stream reasoning applications when (i) we fix jitters and number of instances and vary the periods, (ii) we fix the periods and number of instances and vary the jitters, and (iii) we fix the periods, jitters and vary the number of instances.

In Proceedings of the 15th International Conference on Information Fusion (FUSION). Linköping University Electronic Press.

The main contribution of this paper is a practicalsemantic information integration approach for stream reasoningbased on semantic matching. This is an important functionality for situation awareness applications where temporal reasoning over streams from distributed sources is needed. The integration is achieved by creating a common ontology, specifying the semantic content of streams relative to the ontology and then use semantic matching to find relevant streams. By using semantic mappings between ontologies it is also possible to do semantic matching over multiple ontologies. The complete stream reasoning approach is integrated in the Robot Operating System(ROS) and used in collaborative unmanned aircraft systems missions.

Computer assisted troubleshooting with external interventions is considered. The work is motivated by the task of repairing an automotive vehicle at lowest possible expected cost. The main contribution is a decision theoretic troubleshooting system that is developed to handle external interventions. In particular, practical issues in modeling for troubleshooting are discussed, the troubleshooting system is described, and a method for the efficient probability computations is developed. The troubleshooting systems consists of two parts; a planner that relies on AO* search and a diagnoser that utilizes Bayesian networks (BN). The work is based on a case study of an auxiliary braking system of a modern truck. Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes in the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies. To compute the probabilities, we develop a method based on an algorithm, updateBN, that updates a static BN to account for the external interventions.

Open peer review has been proposed for a number of reasons, in particular, for increasing the transparency of the article selection process for a journal, and for obtaining a broader basis for feedback to the authors and for the acceptance decision. The review discussion may also in itself have a value for the research community. These goals rely on the existence of a lively review discussion, but several experiments with open-process peer review in recent years have encountered the problem of faltering review discussions. The present article addresses the question of how lively review discussion may be fostered by relating the experience of the journal Electronic Transactions on Artificial Intelligence (ETAI) which was an early experiment with open peer review. Factors influencing the discussion activity are identified. It is observed that it is more difficult to obtain lively discussion when the number of contributed articles increases, which implies difficulties for scaling up the open peer review model. Suggestions are made for how this difficulty may be overcome.

Autonomous systems require a lot of information about the environment in which they operate in order to perform different high-level tasks. The information is made available through various sources, such as remote and on-board sensors, databases, GIS, the Internet, etc. The sensory input especially is incrementally available to the systems and can be represented as streams. High-level tasks often require some sort of reasoning over the input data, however raw streaming input is often not suitable for the higher level representations needed for reasoning. DyKnow is a stream processing framework that provides functionalities to represent knowledge needed for reasoning from streaming inputs. DyKnow has been used within a platform for task planning and execution monitoring for UAVs. The execution monitoring is performed using formula progression with monitor rules specified as temporal logic formulas. In this thesis we present an analysis for providing spatio-temporal functionalities to the formula progressor and we extend the formula progression with spatial reasoning in RCC-8. The result implementation is capable of evaluating spatio-temporal logic formulas using progression over streaming data. In addition, a ROS implementation of the formula progressor is presented as a part of a spatio-temporal stream reasoning architecture in ROS.

Edited in collaboration with FoLLI, the Association of Logic, Language and Information, this book collects a set of chapters of the multi-disciplinary project \"Games, actions and Social software\" which was carried out at the Netherlands Institute for Advanced Study in the Humanities and Social Sciences (NIAS) in Wassenaar, from September 2006 through January 2007.&lt;br&gt;The chapters focus on social software and the social sciences, knowledge, belief and action, perception, communication, and cooperation.

In Proceedings of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 478–488. AAAI Press. ISBN:978-1-57735-560-1, 978-1-57735-561-8.Link:http://www.aaai.org/ocs/index.php/KR/KR1...

Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

Gianpaolo Conte and Patrick Doherty.
2011.A Visual Navigation System for UAS Based on Geo-referenced Imagery.

In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVIII-1/C22Proceedings of the International Conference on Unmanned Aerial Vehicle in Geomatics, Zurich, Switzerland, September 14-16, 2011.

We investigate a hybrid between temporal partial-order and forward-chaining planning where each action in a partially ordered plan is associated with a partially defined state. The focus is on centralized planning for multi-agent domains and on loose commitment to the precedence between actions belonging to distinct agents, leading to execution schedules that are flexible where it matters the most. Each agent, on the other hand, has a sequential thread of execution reminiscent of forward-chaining. This results in strong and informative agent-specific partial states that can be used for partial evaluation of preconditions as well as precondition control formulas used as guidance. Empirical evaluation shows the resulting planner to be competitive with TLplan and TALplanner, two other planners based on control formulas, while using a considerably more expressive and flexible plan structure.

This paper addresses the cooperative localization and visual mapping problem with multiple heterogeneous robots. The approach is designed to deal with the challenging large semi-structured outdoors environments in which aerial/ground ensembles are to evolve. We propose the use of heterogeneous visual landmarks, points and line segments, to achieve effective cooperation in such environments. A large-scale SLAM algorithm is generalized to handle multiple robots, in which a global graph maintains the relative relationships between a series of local sub-maps built by the different robots. The key issue when dealing with multiple robots is to find the link between them, and to integrate these relations to maintain the overall geometric consistency; the events that introduce these links on the global graph are described in detail. Monocular cameras are considered as the primary extereoceptive sensor. In order to achieve the undelayed initialization required by the bearing-only observations, the well-known inverse-depth parametrization is adopted to estimate 3D points. Similarly, to estimate 3D line segments, we present a novel parametrization based on anchored PlÃ¼cker coordinates, to which extensible endpoints are added. Extensive simulations show the proposed developments, and the overall approach is illustrated using real-data taken with a helicopter and a ground rover.

Collaborative robotic systems have much to gain by leveraging results from the area of multi-agent systems and in particular agent-oriented software engineering. Agent-oriented software engineering has much to gain by using collaborative robotic systems as a testbed. In this article, we propose and specify a formally grounded generic collaborative system shell for robotic systems and human operated ground control systems. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process implemented in the collaborative system shell. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint problem solving. The system is implemented as a prototype on Unmanned Aerial Vehicle systems and a case study targeting emergency service applications is presented.

In Proceedings of the IEEE International Conference on Robotics and Biomimetic, pages 2955–2962. IEEE conference proceedings. ISBN:978-1-4577-2136-6.DOI:10.1109/ROBIO.2011.6181755.

Cooperative robotic systems, such as unmanned aircraft systems, are becoming technologically mature enough to be integrated into civil society. To gain practical use and acceptance, a verifiable, principled and well-defined foundation for interactions between human operators and autonomous systems is needed. In this paper, we propose and specify such a formally grounded collaboration framework. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint solving. The system is implemented as a prototype on unmanned aerial vehicle systems and a case study targeting emergency service applications is presented.

In this paper we consider rule-based query languages with negation inbodies and heads of rules, traditionally denoted by DATALOG--. Tractable andat the same time intuitive semantics for DATALOG-- has not been provided evenif the area of deductive databases is over 30 years old. In this paper we identifysources of the problem and propose a query language, which we call 4QL.The 4QL language supports a modular and layered architecture and providesa tractable framework for many forms of rule-based reasoning both monotonicand nonmonotonic. As the underpinning principle we assume openness of theworld, which may lead to the lack of knowledge. Negation in rule heads may leadto inconsistencies. To reduce the unknown/inconsistent zones we introduce simpleconstructs which provide means for application-specific disambiguation ofinconsistent information, the use of Local Closed World Assumption (thus alsoClosed World Assumption, if needed), as well as various forms of default anddefeasible reasoning.

In Proceedings of the 3rd International Conference on Knowledge and Systems Engineering (KSE), pages 32–39. IEEE. ISBN:978-1-4577-1848-9.DOI:10.1109/KSE.2011.14.

We develop a Web ontology rule language, called WORL, which combines a variant of OWL 2 RL with eDatalog-with-negation. We disallow the features of OWL 2 RL that play the role of constraints (i.e., the ones that are translated to negative clauses), but allow additional features like negation, the minimal number restriction and unary external checkable predicates to occur in the left hand side of concept inclusion axioms. Some restrictions are adopted to guarantee a translation into eDatalog-with-negation. We also develop the well-founded semantics for WORL and the standard semantics for stratified WORL (SWORL) via translation into eDatalog-with-negation. Both WORL and SWORL have PTime data complexity. In contrast to the existing combined formalisms, in WORL and SWORL negation in concept inclusion axioms is interpreted using nonmonotonic semantics.

A number of popular logical formalisms for representing and reasoning about the abilities of teams or coalitions of agents have been proposed beginning with the Coalition Logic (CL) of Pauly. Ã gotnes et al introduced a means of succinctly expressing quantification over coalitions without compromising the computational complexity of model checking in CL by introducing Quantified Coalition Logic (QCL). QCL introduces a separate logical language for characterizing coalitions in the modal operators used in QCL. Boella et al, increased the representational expressibility of such formalisms by introducing Higher-Order Coalition Logic (HCL), a monadic second-order logic with special set grouping operators. Tractable fragments of HCL suitable for efficient model checking have yet to be identified. In this paper, we relax the monadic restriction used in HCL and restrict ourselves to the diamond operator. We show how formulas using the diamond operator are logically equivalent to second-order formulas. This permits us to isolate and define well-behaved expressive fragments of second-order logic amenable to model-checking in PTime. To do this, we appeal to techniques used in deductive databases and quantifier elimination. In addition, we take advantage of the monotonicity of the effectivity function resulting in exponentially more succinct representation of models. The net result is identification of highly expressible fragments of a generalized HCL where model checking can be done efficiently in PTime.

It is known that the OWL 2 RL Web Ontology Language Profile has PTime data complexity and can be translated into Datalog. However, a knowledge base in OWL 2 RL may be unsatisfiable. The reason is that, when translated into Datalog, the result may consist of a Datalog program and a set of constraints in the form of negative clauses. In this paper we first identify a maximal fragment of OWL 2 RL called OWL 2 RL<sup>â+â</sup>with the property that every knowledge base expressed in this fragment can be translated into a Datalog program and hence is satisfiable. We then propose some extensions of OWL 2 RL and OWL 2 RL<sup>â+â</sup> that still have PTime data complexity.

The study of cooperation among agents is of central interest in multi-agent systems research. A popular way to model cooperation is through coalitional game theory. Much research in this area has had limited practical applicability as regards real-world multi-agent systems due to the fact that it assumes<em>deterministic</em> payoffs to coalitions and in addition does not apply to multi-agent environments that are<em>stochastic</em> in nature. In this paper, we propose a novel approach to modeling such scenarios where coalitional games will be contextualized through the use of logical expressions representing environmental and other state, and probability distributions will be placed on the space of contexts in order to model the stochastic nature of the scenarios. More formally, we present a formal representation language for representing contextualized coalitional games embedded in stochastic environments and we define and show how to compute <em>expected Shapley values</em> in such games in a computationally efficient manner. We present the value of the approach through an example involving robotics assistance in emergencies.

<em>In this paper we study automated reasoning in the modal logic CPDLreg which is a combination of CPDL (Propositional Dynamic Logic with Converse) and REGc (Regular Grammar Logic with Converse). The logic CPDL is widely used in many areas, including program verification, theory of action and change, and knowledge representation. On the other hand, the logic REGc is applicable in reasoning about epistemic states and ontologies (via Description Logics). The modal logic CPDLreg can serve as a technical foundation for reasoning about agency. Even very rich multi-agent logics can be embedded into CPDLreg via a suitable translation. Therefore, CPDLreg can serve as a test bed to implement and possibly verify new ideas without providing specific automated reasoning techniques for the logic in question. This process can to a large extent be automated. The idea of embedding various logics into CPDLreg is illustrated on a rather advanced logic TEAMLOGK designed to specify teamwork in multi-agent systems. Apart from defining informational and motivational attitudes of groups of agents, TEAMLOGK allows for grading agents' beliefs, goals and intentions. The current paper is a companion to our paper (Dunin-KÄplicz et al., 2010a). The main contribution are proofs of soundness and completeness of the tableau calculus for CPDLreg provided in (Dunin-KÄplicz et al., 2010a).</em>

Grammar logics were introduced by FariÃ±as del Cerro and Penttonen in 1988 and have been widely studied. In this paper we consider regular grammar logics with converse (<em>REG</em> <sup><em>c</em> </sup>logics) and present sound and complete tableau calculi for the general satisfiability problem of <em>REG</em> <sup><em>c</em> </sup>logics and the problem of checking consistency of an ABox w.r.t. a TBox in a <em>REG</em> <sup><em>c</em> </sup>logic. Using our calculi we develop ExpTime (optimal) tableau decision procedures for the mentioned problems, to which various optimization techniques can be applied. We also prove a new result that the data complexity of the instance checking problem in <em>REG</em> <sup><em>c</em></sup>logics is coNP-complete.

The paper discusses properties of 4QL, a DATALOGÂ¬Â¬-like query language, originally outlined by MaÅuszyÂ´nski and SzaÅas (MaÅuszyÂ´nski &amp; SzaÅas, 2011). 4QL allows one to use rules with negation in heads and bodies of rules. It is based on a simple and intuitive semantics and provides uniform tools for âlightweightâ versions of known forms of nonmonotonic reasoning. Negated literals in heads of rules may naturally lead to inconsistencies. On the other hand, rules do not have to attach meaning to some literals. Therefore 4QL is founded on a four-valued semantics, employing the logic introduced in (MaÅuszyÂ´nski et al., 2008; VitÃ³ria et al., 2009) with truth values: âtrueâ, âfalseâ, âinconsistentâ and âunknownâ. In addition, 4QL is tractable w.r.t. data complexity and captures PTIME queries. Even though DATALOGÂ¬Â¬ is known as a concept for the last 30 years, to our best knowledge no existing approach enjoys these properties. In the current paper we:<ul><li>investigate properties of well-supported models of 4QL</li><li>prove the correctness of the algorithm for computing well-supported models</li><li>show that 4QL has PTIME data complexity and captures PTIME.</li></ul>

Autonomous system needs to do a great deal of reasoning during execution in order to provide timely reactions to changes in their environment. Data needed for this reasoning process is often provided through a number of sensors. One approach for this kind of reasoning is evaluation of temporal logical formulas through progression. To evaluate these formulas it is necessary to provide relevant data for each symbol in a formula. Mapping relevant data to symbols in a formula could be done manually, however as systems become more complex it is harder for a designer to explicitly state and maintain thismapping. Therefore, automatic support for mapping data from sensors to symbols would make system more flexible and easier to maintain.DyKnow is a knowledge processing middleware which provides the support for processing data on different levels of abstractions. The output from the processing components in DyKnow is in the form of streams of information. In the case of DyKnow, reasoning over incrementally available data is done by progressing metric temporal logical formulas. A logical formula contains a number of symbols whose values over time must be collected and synchronized in order to determine the truth value of the formula. Mapping symbols in formula to relevant streams is done manually in DyKnow. The purpose of this matching is for each variable to find one or more streams whose content matches the intended meaning of the variable.This thesis analyses and provides a solution to the process of semantic matching. The analysis is mostly focused on how the existing semantic technologies such as ontologies can be used in this process. The thesis also analyses how this process can be used for matching symbols in a formula to content of streams on distributed and heterogeneous platforms. Finally, the thesis presents an implementation in the Robot Operating System (ROS). The implementation is tested in two case studies which cover a scenario where there is only a single platform in a system and a scenario where there are multiple distributed heterogeneous platforms in a system.The conclusions are that the semantic matching represents an important step towards fully automatized semantic-based stream reasoning. Our solution also shows that semantic technologies are suitable for establishing machine-readable domain models. The use of these technologies made the semantic matching domain and platform independent as all domain and platform specific knowledge is specified in ontologies. Moreover, semantic technologies provide support for integration of data from heterogeneous sources which makes it possible for platforms to use streams from distributed sources.

The Association for the Advancement of Artificial Intelligence presented the 2011 Spring Symposium Series Monday through Wednesday, March 21-23, 2011, at Stanford University. This report summarizes the eight symposia.

The range of missions performed by Unmanned Aircraft Systems (UAS) has been steadily growing in the past decades thanks to continued development in several disciplines. The goal of increasing the autonomy of UAS's is widening the range of tasks which can be carried out without, or with minimal, external help. This thesis presents methods for increasing specific aspects of autonomy of UAS's operating both in outdoor and indoor environments where cameras are used as the primary sensors.First, a method for fusing color and thermal images for object detection, geolocation and tracking for UAS's operating primarily outdoors is presented. Specifically, a method for building saliency maps where human body locations are marked as points of interest is described. Such maps can be used in emergency situations to increase the situational awareness of first responders or a robotic system itself. Additionally, the same method is applied to the problem of vehicle tracking. A generated stream of geographical locations of tracked vehicles increases situational awareness by allowing for qualitative reasoning about, for example, vehicles overtaking, entering or leaving crossings.Second, two approaches to the UAS indoor localization problem in the absence of GPS-based positioning are presented. Both use cameras as the main sensors and enable autonomous indoor ight and navigation. The first approach takes advantage of cooperation with a ground robot to provide a UAS with its localization information. The second approach uses marker-based visual pose estimation where all computations are done onboard a small-scale aircraft which additionally increases its autonomy by not relying on external computational power.

Unmanned aircraft systems (UASs) are an important future technology with early generations already being used in many areas of application encompassing both military and civilian domains. This thesis proposes a number of integration techniques for combining control-based navigation with more abstract path planning functionality for UASs. These techniques are empirically tested and validated using an RMAX helicopter platform used in the UASTechLab at LinkÃ¶ping University. Although the thesis focuses on helicopter platforms, the techniques are generic in nature and can be used in other robotic systems.At the control level a navigation task is executed by a set of control modes. A framework based on the abstraction of hierarchical concurrent state machines for the design and development of hybrid control systems is presented. The framework is used to specify reactive behaviors and for sequentialisation of control modes. Selected examples of control systems deployed on UASs are presented. Collision-free paths executed at the control level are generated by path planning algorithms.We propose a path replanning framework extending the existing path planners to allow dynamic repair of flight paths when new obstacles or no-fly zones obstructing the current flight path are detected. Additionally, a novel approach to selecting the best path repair strategy based on machine learning technique is presented. A prerequisite for a safe navigation in a real-world environment is an accurate geometrical model. As a step towards building accurate 3D models onboard UASs initial work on the integration of a laser range finder with a helicopter platform is also presented.Combination of the techniques presented provides another step towards building comprehensive and robust navigation systems for future UASs.

The problem of determining who should do what given a set of tasks and a set of agents is called the task allocation problem. The problem occurs in many multi-agent system applications where a workload of tasks should be shared by a number of agents. In our case, the task allocation problem occurs as an integral part of a larger problem of determining if a task can be delegated from one agent to another.Delegation is the act of handing over the responsibility for something to someone. Previously, a theory for delegation including a delegation speech act has been specified. The speech act specifies the preconditions that must be fulfilled before the delegation can be carried out, and the postconditions that will be true afterward. To actually use the speech act in a multi-agent system, there must be a practical way of determining if the preconditions are true. This can be done by a process that includes solving a complex task allocation problem by the agents involved in the delegation.In this thesis a constraint-based task specification formalism, a complex task allocation algorithm for allocating tasks to unmanned aerial vehicles and a generic collaborative system shell for robotic systems are developed. The three components are used as the basis for a collaborative unmanned aircraft system that uses delegation for distributing and coordinating the agents' execution of complex tasks.

The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them.In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name.In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase.Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities.The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set.The software that has been implemented and used in this project has been implemented in C.

This licentiate thesis considers computer-assisted troubleshooting of complex products such as heavy trucks. The troubleshooting task is to find and repair all faulty components in a malfunctioning system. This is done by performing actions to gather more information regarding which faults there can be or to repair components that are suspected to be faulty. The expected cost of the performed actions should be as low as possible.The work described in this thesis contributes to solving the troubleshooting task in such a way that a good trade-off between computation time and solution quality can be made. A framework for troubleshooting is developed where the system is diagnosed using non-stationary dynamic Bayesian networks and the decisions of which actions to perform are made using a new planning algorithm for Stochastic Shortest Path Problems called Iterative Bounding LAO*.It is shown how the troubleshooting problem can be converted into a Stochastic Shortest Path problem so that it can be efficiently solved using general algorithms such as Iterative Bounding LAO*. New and improved search heuristics for solving the troubleshooting problem by searching are also presented in this thesis.The methods presented in this thesis are evaluated in a case study of an auxiliary hydraulic braking system of a modern truck. The evaluation shows that the new algorithm Iterative Bounding LAO* creates troubleshooting plans with a lower expected cost faster than existing state-of-the-art algorithms in the literature. The case study shows that the troubleshooting framework can be applied to systems from the heavy vehicles domain.

Surveillance is an important application for unmanned aerial vehicles (UAVs). The sensed information often has high priority and it must be made available to human operators as quickly as possible. Due to obstacles and limited communication range, it is not always possible to transmit the information directly to the base station. In this case, other UAVs can form a relay chain between the surveillance UAV and the base station. Determining suitable positions for such UAVs is a complex optimization problem in and of itself, and is made even more diï¬cult by communication and surveillance constraints.To solve diï¬erent variations of ï¬nding positions for UAVs for surveillance of one target, two new algorithms have been developed. One of the algorithms is developed especially for ï¬nding a set of relay chains oï¬ering diï¬erent trade-oï¬s between the number of UAVsand the quality of the chain. The other algorithm is tailored towards ï¬nding the highest quality chain possible, given a limited number of available UAVs.Finding the optimal positions for surveillance of several targets is more diï¬cult. A study has been performed, in order to determine how the problems of interest can besolved. It turns out that very few of the existing algorithms can be used due to the characteristics of our speciï¬c problem. For this reason, an algorithm for quickly calculating positions for surveillance of multiple targets has been developed. This enables calculation of an initial chain that is immediately made available to the user, and the chain is then incrementally optimized according to the userâs desire.

This note describes how the notion of nonmonotonic reasoning emerged in Artificial Intelligence from the mid-1960s to 1980. It gives particular attention to the interplay between three kinds of activities: design of high-level programming systems for AI, design of truth-maintenance systems, and the development of nonmonotonic logics. This was not merely a development from logic to implementation: in several cases there was a development from a system design to a corresponding logic. The article concludes with some reflections on the roles and relationships between logicist theory and system design in AI, and in particular in Knowledge Representation.

In Proceedings of the 4th International KES Symposium on Agents and Multi-agent Systems ? Technologies and Applications (KES-AMSTA), pages 152–162. In series: Lecture Notes in Artificial Intelligence #6070. Springer. ISBN:978-3-642-13479-1.DOI:10.1007/978-3-642-13480-7_17.

Description logics [1] refer to a family of formalisms concentrated around concepts, roles and individuals. They are used in many multiagent and semantic web applications as a foundation for specifying knowledge bases and reasoning about them. One of widely applied description logics is <em>SHIQ</em><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char53.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char48.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char49.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char51.png\" /> [7,8]. In the current paper we address the problem of inconsistent knowledge. Inconsistencies may naturally appear in the considered application domains, for example as a result of fusing knowledge from distributed sources. We define three three-valued paraconsistent semantics for <em>SHIQ</em><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char53.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char48.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char49.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char51.png\" />, reflecting different meanings of concept inclusion of practical importance. We also provide a quite general syntactic condition of safeness guaranteeing satisfiability of a knowledge base w.r.t. three-valued semantics and define a faithful translation of our formalism into a suitable version of a two-valued description logic. Such a translation allows one to use existing tools and <em>SHIQ</em><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char53.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char48.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char49.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char51.png\" /> reasoners to deal with inconsistent knowledge.

Description logics (DLs) are a family of knowledge representation languages which can be used to represent the terminological knowledge of an application domain in a structured and formally well-understood way. DLs can be used, for example, for conceptual modeling or as ontology languages. In fact, OWL (Web Ontology Language), recommended by W3C, is based on description logics. In the current paper we give the first direct ExpTime (optimal) tableau decision procedure, which is not based on transformation or on the pre-completion technique, for checking satisfiability of a knowledge base in the description logic <em>SH</em><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char53.png\" /><img src=\"http://www.springerlink.com/jsMath/fonts/cmsy10/alpha/120/char48.png\" />. Our procedure uses sound global caching and can be implemented as an extension of the highly optimized tableau prover TGC to obtain an efficient program for the mentioned satisfiability problem.

This article introduces and uses a representation of defeasible inheritance networks where links in the network are viewed as propositions, and where defeasible links are tagged with a quantitative indication of the proportion of exceptions, called the doubt index. This doubt index is used for restricting the length of the chains of inference. The representation also introduces the use of defeater literals that disable the chaining of subsumption links. The use of defeater literals replaces the use of negative defeasible inheritance links, expressing \"most A are not B\". The new representation improves the expressivity significantly. Inference in inheritance networks is defined by a combination of axioms that constrain the contents of network extensions, a heuristic restriction that also has that effect, and a nonmonotonic operation of minimizing the set of defeater literals while retaining consistency. We introduce an underlying semantics that defines the meaning of literals in a network, and prove that the axioms are sound with respect to this semantics. We also discuss the conditions for obtaining completeness. Traditional concepts, assumptions and issues in research on nonmonotonic or defeasible inheritance are reviewed in the perspective of this approach.

We reformulate Pratts tableau decision procedure of checking satisfiability of a set of formulas in PDL. Our formulation is simpler and its implementation is more direct. Extending the method we give the first Ex PT m E (optimal) tableau decision procedure not based on transformation for checking consistency of an ABox w.r.t. a TBox in PDL (here, PDL is treated as a description logic). We also prove a new result that the data complexity of the instance checking problem in PDL is coNP-complete.

We provide an overview of ongoing research which targets development of a principled framework for mixed-initiative interaction with unmanned aircraft systems (UAS). UASs are now becoming technologically mature enough to be integrated into civil society. Principled interaction between UASs and human resources is an essential component in their future uses in complex emergency services or bluelight scenarios. In our current research, we have targeted a triad of fundamental, interdependent conceptual issues: delegation, mixed- initiative interaction and adjustable autonomy, that is being used as a basis for developing a principled and well-defined framework for interaction. This can be used to clarify, validate and verify different types of interaction between human operators and UAS systems both theoretically and practically in UAS experimentation with our deployed platforms.

We consider a constrained optimization problem with mixed integer and real variables. It models optimal placement of communications relay nodes in the presence of obstacles. This problem is widely encountered, for instance, in robotics, where it is required to survey some target located in one point and convey the gathered information back to a base station located in another point. One or more unmanned aerial or ground vehicles (UAVs or UGVs) can be used for this purpose as communications relays. The decision variables are the number of unmanned vehicles (UVs) and the UV positions. The objective function is assumed to access the placement quality. We suggest one instance of such a function which is more suitable for accessing UAV placement. The constraints are determined by, firstly, a free line of sight requirement for every consecutive pair in the chain and, secondly, a limited communication range. Because of these requirements, our constrained optimization problem is a difficult multi-extremal problem for any fixed number of UVs. Moreover, the feasible set of real variables is typically disjoint. We present an approach that allows us to efficiently find a practically acceptable approximation to a global minimum in the problem of optimal placement of communications relay nodes. It is based on a spatial discretization with a subsequent reduction to a shortest path problem. The case of a restricted number of available UVs is also considered here. We introduce two label correcting algorithms which are able to take advantage of using some peculiarities of the resulting restricted shortest path problem. The algorithms produce a Pareto solution to the two-objective problem of minimizing the path cost and the number of hops. We justify their correctness. The presented results of numerical 3D experiments show that our algorithms are superior to the conventional Bellman-Ford algorithm tailored to solving this problem.

In Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), pages 1913–1920. In series: Proceedings - IEEE International Conference on Robotics and Automation #2010. IEEE conference proceedings. ISBN:978-1-4244-5038-1.DOI:10.1109/ROBOT.2010.5509203.

We present a navigation system for autonomous indoor flight of micro-scale Unmanned Aircraft Systems (UAS) which is based on a method for accurate monocular vision pose estimation. The method makes use of low cost artificial landmarks placed in the environment and allows for fully autonomous flight with all computation done on-board a UAS on COTS hardware. We provide a detailed description of all system components along with an accuracy evaluation and a time profiling result for the pose estimation method. Additionally, we show how the system is integrated with an existing micro-scale UAS and provide results of experimental autonomous flight tests. To our knowledge, this system is one of the first to allow for complete closed-loop control and goal-driven navigation of a micro-scale UAS in an indoor setting without requiring connection to any external entities.

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal kpl language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.

In Proceedings of the AAAI Spring Symposium on Embedded Reasoning: Intelligence in Embedded Systems (ER).

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. In order to make use of diverse reasoning modules in such systems, issues of integration such as sensor data flow and information flow between such modules has to be taken into account. The DyKnow framework is a tool with a formal basis that pragmatically deals with many of the architectural issues which arise in such systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. DyKnow has proven to be quite robust and widely applicable to different aspects of hybrid software architectures for robotics.In this paper, we describe the DyKnow framework and show how it is integrated and used in unmanned aerial vehicle systems developed in our group. In particular, we focus on issues pertaining to the sense-reasoning gap and the symbol grounding problem and the use of DyKnow as a means of generating semantic structures representing situational awareness for such systems. We also discuss the use of DyKnow in the context of automated planning, in particular execution monitoring.

Detecting and isolating multiple faults is a computationally expensive task. It typically consists of computing a set of tests and then computing the diagnoses based on the test results. This paper describes FlexDx, a reconfigurable diagnosis framework which reduces the computational burden while retaining the isolation performance by only running a subset of all tests that is sufficient to find new conflicts. Tests in FlexDx are thresholded residuals used to indicate conflicts in the monitored system. Special attention is given to the issues introduced by a reconfigurable diagnosis framework. For example, tests are added and removed dynamically, tests are partially performed on historic data, and synchronous and asynchronous processing are combined. To handle these issues FlexDx has been implemented using DyKnow, a stream-based knowledge processing middleware framework. Concrete methods for each component in the FlexDx framework are presented. The complete approach is exemplified on a dynamic system which clearly illustrates the complexity of the problem and the computational gain of the proposed approach.

In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI). In series: Frontiers in Artificial Intelligence and Applications #215. IOS Press. ISBN:978-1-60750-605-8.DOI:10.3233/978-1-60750-606-5-183.

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based knowledge processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment. DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many highlevel reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.

In Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), pages 1063–1069. IEEE conference proceedings. ISBN:978-1-4244-7814-9.DOI:10.1109/ICARCV.2010.5707967.

As unmanned aerial vehicle (UAV) applications are becoming more complex and covering larger physical areas there is an increasing need for multiple UAVs to cooperatively solve problems. To produce more complete and accurate information about the environment we present the DyKnow Federation framework for distributed fusion among collaborative UAVs. A federation is created and maintained using a multi-agent delegation framework which allows high-level specification and reasoning about resource bounded cooperative problem solving. When the federation is set up, local information is transparently shared between the agents according to specification. The work is presented in the context of a multi-UAV traffic monitoring scenario.

Mission planning for collaborative Unmanned Aircraft Systems (UAS:s) is a complex topic which involves trade-offs between the degree of centralization or decentralization required, the degree of abstraction in which plans are generated, and the degree to which such plans are distributed among participating UAS:s. In realistic environments such as those found in naturaland man-made catastrophes where emergency services personnelare involved, a certain degree of centralization and abstractionis necessary in order for those in charge to understand andeventually sign off on potential plans. It is also quite often thecase that unconstrained distribution of actions is inconsistentwith the loosely coupled interactions and dependencies whicharise between collaborating systems. In this article, we presenta new planning algorithm for collaborative UAS:s based oncombining ideas from forward chaining planning with partialorderplanning leading to a new hybrid partial order forwardchaining(POFC) framework which meets the requirements oncentralization, abstraction and distribution we find in realisticemergency services settings.

An important use of unmanned aerial vehicles is surveillance of distant targets, where sensor information must quickly be transmitted back to a base station. In many cases, high uninterrupted bandwidth requires line-of-sight between sender and transmitter to minimize quality degradation. Communication range is typically limited, especially when smaller UAVs are used. Both problems can be solved by creating relay chains for surveillance of a single target, and relay trees for simultaneous surveillance of multiple targets. In this paper, we show how such chains and trees can be calculated. For relay chains we create a set of chains offering different trade-offs between the number of UAVs in the chain and the chainâs cost. We also show new results on how relay trees can be quickly calculated and then incrementally improved if necessary. Encouraging empirical results for improvement of relay trees are presented.

In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), pages 341–346. In series: Frontiers in Artificial Intelligence and Applications #215. IOS Press. ISBN:978-1-60750-605-8, 978-1-60750-606-5.DOI:10.3233/978-1-60750-606-5-341.

Iterative Bounding LAO* is a new algorithm for epsilon- optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given initial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.

Partially ordered plan structures are highly suitable for centralized multi-agent planning, where plans should be minimally constrained in terms of precedence between actions performed by different agents. In many cases, however, any given agent will perform its own actions in strict sequence. We take advantage of this fact to develop a hybrid of temporal partial order planning and forward-chaining planning. A sequence of actions is constructed for each agent and linked to other agents' actions by a partially ordered precedence relation as required. When agents are not too tightly coupled, this structure enables the generation of partial but strong information about the state at the end of each agent's action sequence. Such state information can be effectively exploited during search. A prototype planner within this framework has been implemented, using precondition control formulas to guide the search process.

There is a natural generalization of an indiscernibility relation used in rough set theory, where rather than partitioning the universe of discourse into indiscernibility classes, one can consider a covering of the universe by similarity-based neighborhoods with lower and upper approximations of relations defined via the neighborhoods. When taking this step, there is a need to tune approximate reasoning to the desired accuracy. We provide a framework for analyzing self-adaptive knowledge structures. We focus on studying the interaction between inputs and output concepts in approximate reasoning. The problems we address are: -given similarity relations modeling approximate concepts, what are similarity relations for the output concepts that guarantee correctness of reasoning? -assuming that output similarity relations lead to concepts which are not accurate enough, how can one tune input similarities?

In natural language we often use graded concepts, reflecting different intensity degrees of certain features. Whenever such concepts appear in a given real-life context, they need to be appropriately expressed in its models. In this paper, we provide a framework which allows for extending the BGI model of agency by grading beliefs, goals and intentions. We concentrate on TEAMLOG [6, 7, 8, 9, 12] and provide a complexity-optimal decision method for its graded version TEAMLOG(K) by translating it into CPDLreg (propositional dynamic logic with converse and \"inclusion axioms\" characterized by regular languages). We also develop a tableau calculus which leads to the first EXPTIME (optimal) tableau decision procedure for CPDLreg. As CPDLreg is suitable for expressing complex properties of graded operators, the procedure can also be used as a decision tool for other multiagent formalisms.

In this paper we present a framework for fusing approximate knowledge obtained from various distributed, heterogenous knowledge sources. This issue is substantial in modeling multi-agent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. In paper [5] we have focused on defining general mechanism for knowledge fusion. Next, the techniques ensuring tractability of fusing knowledge expressed as a Horn subset of propositional dynamic logic were developed in [13,16]. Propositional logics may seem too weak to be useful in real-world applications. On the other hand, propositional languages may be viewed as sublanguages of first-order logics which serve as a natural tool to define concepts in the spirit of description logics [2]. These notions may be further used to define various ontologies, like e. g. those applicable in the Semantic Web. Taking this step, we propose a framework, in which our Horn subset of dynamic logic is combined with deductive database technology. This synthesis is formally implemented in the framework of HSPDL architecture. The resulting knowledge fusion rules are naturally applicable to real-world data.

When unmanned aerial vehicles (UAVs) are used for surveillance, information must often be transmitted to a base station in real time. However, limited communication ranges and the common requirement of free line of sight may make direct transmissions from distant targets impossible. This problem can be solved using relay chains consisting of one or more intermediate relay UAVs. This leads to the problem of positioning such relays given known obstacles, while taking into account a possibly mission-specific quality measure. The maximum quality of a chain may depend strongly on the number of UAVs allocated. Therefore, it is desirable to either generate a chain of maximum quality given the available UAVs or allow a choice from a spectrum of Pareto-optimal chains corresponding to different trade-offs between the number of UAVs used and the resulting quality. In this article, we define several problem variations in a continuous three-dimensional setting. We show how sets of Pareto-optimal chains can be generated using graph search and present a new label-correcting algorithm generating such chains significantly more efficiently than the best-known algorithms in the literature. Finally, we present a new dual ascent algorithm with better performance for certain tasks and situations.

In this paper we investigate a technique for fusing approximate knowledge obtained from distributed, heterogeneous information sources. This issue is substantial, e.g., in modeling multiagent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. Information exchange, leading ultimately to knowledge fusion, is a natural and vital ingredient of this process. We use a generalization of rough sets and relations [30], which depends on allowing arbitrary similarity relations. The starting point of this research is [6], where a framework for knowledge fusion in multiagent systems is introduced. Agents individual perceptual capabilities are represented by similarity relations, further aggregated to express joint capabilities of teams, This aggregation, expressing a shift from individual to social level of agents activity, has been formalized by means of dynamic logic. The approach of Doherty et al. (2007) [6] uses the full propositional dynamic logic, which does not guarantee tractability of reasoning. Our idea is to adapt the techniques of Nguyen [26-28] to provide an engine for tractable approximate database querying restricted to a Horn fragment of serial dynamic logic. We also show that the obtained formalism is quite powerful in applications.

We describe an approach to integrate dialogue management, machine-learning and action planning in a system for dialogue between a human and a robot. Case-based techniques are used because they permit life-long learning from experience and demand little prior knowledge and few static hand-written structures. This approach has been developed through the work on an experimental dialogue system, called CEDERIC, that is connected to an unmanned aerial vehicle (UAV). A single case base and case-based reasoning engine is used both for understanding and for planning actions by the UAV. Dialogue experiments both with experienced and novice users, where the users have solved tasks by dialogue with this system, showed very adequate success rates.

This thesis looks into how one could use Hierarchal Temporal Memory (HTM) networks to generate models that could be used as trading algorithms. The thesis begins with a brief introduction to algorithmic trading and commonly used concepts when developing trading algorithms. The thesis then proceeds to explain what an HTM is and how it works. To explore whether an HTM could be used to generate models that could be used as trading algorithms, the thesis conducts a series of experiments. The goal of the experiments is to iteratively optimize the settings for an HTM and try to generate a model that when used as a trading algorithm would have more profitable trades than losing trades. The setup of the experiments is to train an HTM to predict if it is a good time to buy some shares in a security and hold them for a fixed time before selling them again. A fair amount of the models generated during the experiments was profitable on data the model have never seen before, therefore the author concludes that it is possible to train an HTM so it can be used as a profitable trading algorithm.

Engineering autonomous agents that display rational and goal-directed behavior in dynamic physical environments requires a steady flow of information from sensors to high-level reasoning components. However, while sensors tend to generate noisy and incomplete quantitative data, reasoning often requires crisp symbolic knowledge. The gap between sensing and reasoning is quite wide, and cannot in general be bridged in a single step. Instead, this task requires a more general approach to integrating and organizing multiple forms of information and knowledge processing on different levels of abstraction in a structured and principled manner. We propose knowledge processing middleware as a systematic approach to organizing such processing. Desirable properties are presented and motivated. We argue that a declarative stream-based system is appropriate for the required functionality and present DyKnow, a concrete implemented instantiation of stream-based knowledge processing middleware with a formal semantics. Several types of knowledge processes are defined and motivated in the context of a UAV traffic monitoring application. In the implemented application, DyKnow is used to incrementally bridge the sense-reasoning gap and generate partial logical models of the environment over which metric temporal logical formulas are evaluated. Using such formulas, hypotheses are formed and validated about the type of vehicles being observed. DyKnow is also used to generate event streams representing for example changes in qualitative spatial relations, which are used to detect traffic violations expressed as declarative chronicles.

When unmanned aerial vehicles (UAVs) are used to survey distant targets, it is important to transmit sensor information back to a base station. As this communication often requires high uninterrupted bandwidth, the surveying UAV often needs afree line-of-sight to the base station, which can be problematic in urban or mountainous areas. Communication ranges may also belimited, especially for smaller UAVs. Though both problems can be solved through the use of relay chains consisting of one or more intermediate relay UAVs, this leads to a new problem: Where should relays be placed for optimum performance? We present two new algorithms capable of generating such relay chains, one being a dual ascent algorithm and the other a modification of the Bellman-Ford algorithm. As the priorities between the numberof hops in the relay chain and the cost of the chain may vary, wecalculate chains of different lengths and costs and let the ground operator choose between them. Several different formulations for edge costs are presented. In our test cases, both algorithms are substantially faster than an optimized version of the original Bellman-Ford algorithm, which is used for comparison.

In this Master Thesis the possibility to efficiently divide a graph into spanner islands is examined. Spanner islands are islands of the graph that fulfill the spanner condition, that the distance between two nodes via the edges in the graph cannot be too far, regulated by the stretch constant, compared to the Euclidian distance between them. In the resulting division the least number of nodes connecting to other islands is sought-after. Different heuristics are evaluated with the conclusion that for dense graphs a heuristic using MAX-FLOW to divide problematic nodes gives the best result whereas for sparse graphs a heuristic using the single-link clustering method performs best. The problem of finding a spanner path, a path fulfilling the spanner condition, between two nodes is also investigated. The problem is proven to be NP-complete for a graph of size n if the spanner constant is greater than n^(1+1/k)*k^0.5 for some integer k. An algorithm with complexity O(2^(0.822n)) is given. A special type of graph where all the nodes are located on integer locations along the real line is investigated. An algorithm to solve this problem is presented with a complexity of O(2^((c*log n)^2))), where c is a constant depending only on the spanner constant. For instance, the complexity O(2^((5.32*log n)^2))) can be reached for stretch 1.5.

In this paper we investigate a technique for fusing approximate knowledge obtained from distributed, heterogeneous information sources. We use a generalization of rough sets and relations [14], which depends on allowing arbitrary similarity relations. The starting point of this research is [2], where a framework for knowledge fusion in multi-agent systems is introduced. Agentâs individual perceptual capabilities are represented by similarity relations, further aggregated to express joint capabilities of teams. This aggregation, allowing a shift from individual to social level, has been formalized by means of dynamic logic. The approach of [2] uses the full propositional dynamic logic, not guaranteeing the tractability of reasoning. Therefore the results of [11, 12, 13] are adapted to provide a technical engine for tractable approximate database querying restricted to a Horn fragment of serial PDL. We also show that the obtained formalism is quite powerful in applications.

In Proceedings of the 1st International Conference on Knowlegde and Systems Engineering (KSE), pages 207–214. IEEE Computer Society. ISBN:978-1-4244-5086-2.DOI:10.1109/KSE.2009.12.

We give a novel tableau calculus and an optimal (EXPTIME) tableau decision procedure based on the calculus for the satisfiability problem of propositional dynamic logic with converse. Our decision procedure is formulated with global caching and can be implemented together with useful optimization techniques.

In Proceedings of the 14th International Symposium on Robotics Research (ISRR), pages 681–696. In series: Springer Tracts in Advanced Robotics #70. Springer. ISBN:978-3-642-19456-6.DOI:10.1007/978-3-642-19457-3_40.

This paper addresses the cooperative localization and visual mapping problem for multiple aerial and ground robots.We propose the use of heterogeneous visual landmarks, points and line segments. A large-scale SLAM algorithm is generalized to manage multiple robots, in which a global graph maintains the topological relationships between a series of local sub-maps built by the different robots. Only single camera setups are considered: in order to achieve undelayed initialization, we present a novel parametrization for lines based on anchored PlÃ¼cker coordinates, to which we add extensible endpoints to enhance their representativeness. The built maps combine such lines with 3D points parametrized in inverse-depth. The overall approach is evaluated with real-data taken with a helicopter and a ground rover in an abandoned village.

In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1535–1540. IEEE conference proceedings. ISBN:978-1-4244-3803-7.DOI:10.1109/IROS.2009.5354335.

A large-scale mapping approach is combined with multiple robots events to achieve cooperative mapping. The mapping approach used is based on hierarchical SLAM -global level and local maps-, which is generalized for the multi-robot case. In particular, the consequences of multi-robot loop closing events (common landmarks detection and relative pose measurement between robots) are analyzed and managed at a global level. We present simulation results for each of these events using aerial and ground robots, and experimental results obtained with ground robots.

In the current paper we consider theories with vocabulary containing a number of binary and unary relation symbols. Binary relation symbols represent labeled edges of a graph and unary relations represent unique annotations of the graphâs nodes. Such theories, which we call <em>annotation theories</em>, can be used in many applications, including the formalization of argumentation, approximate reasoning, semantics of logic programs, graph coloring, etc. We address a number of problems related to annotation theories over finite models, including satisfiability, querying problem, specification of preferred models and model checking problem. We show that most of considered problems are NPTime- or co-NPTime-complete. In order to reduce the complexity for particular theories, we use second-order quantifier elimination. To our best knowledge none of existing methods works in the case of annotation theories. We then provide a new second-order quantifier elimination method for stratified theories, which is successful in the considered cases. The new result subsumes many other results, including those of [2, 28, 21].

In Proceedings of the 2nd IFAC Workshop on Dependable Control of Discrete Systems (DCDS), pages 251–256. ISBN:978-390266144-9.DOI:10.3182/20090610-3-IT-4004.00048.

We consider computer assisted troubleshooting of complex systems, where the objective is to identify the cause of a failure and repair the system at as low expected cost as possible. Three main challenges are: the need for disassembling the system during troubleshooting, the difficulty to verify that the system is fault free, and the dependencies in between components and observations. We present a method that can return a response anytime, which allows us to obtain the best result given the available time. The work is based on a case study of an auxiliary braking system of a modern truck. We highlight practical issues related to model building and troubleshooting in a real environment.

We present a language for defining paraconsistent rough sets and reasoning about them. Our framework relates and brings together two major fields: rough sets [23] and paraconsistent logic programming [9]. To model inconsistent and incomplete information we use a four-valued logic. The language discussed in this paper is based on ideas of our previous work [21, 32, 22] developing a four-valued framework for rough sets. In this approach membership function, set containment and set operations are four-valued, where logical values are t (true), f (false), i (inconsistent) and u (unknown). We investigate properties of paraconsistent rough sets as well as develop a paraconsistent rule language, providing basic computational machinery for our approach.

We consider computer assisted troubleshooting of automotive vehicles, where the objective is to repair the vehicle at as low expected cost as possible.The work has three main contributions: a troubleshooting method that applies to troubleshooting in real environments, the discussion on practical issues in modeling for troubleshooting, and the efficient probability computations.The work is based on a case study of an auxiliary braking system of a modern truck.We apply a decision theoretic approach, consisting of a planner and a diagnoser.Two main challenges in troubleshooting automotive vehicles are the need for disassembling the vehicle during troubleshooting to access parts to repair, and the difficulty to verify that the vehicle is fault free. These facts lead to that probabilities for faults and for future observations must be computed for a system that has been subject to external interventions that cause changes the dependency structure. The probability computations are further complicated due to the mixture of instantaneous and non-instantaneous dependencies.To compute the probabilities, we develop a method based on an algorithm, <em>updateBN</em>, that updates a static BN to account for the external interventions.

In Proceedings of the Scheduling and Planning Applications Workshop (SPARK) at the 19th International Conference on Automated Planning and Scheduling (ICAPS).

In this paper we study the problem of incremental fault diagnosis and repair of mechatronic systems where the task is to choose actions such that the expected cost of repair is minimal. This is done by interleaving acting with the generation of partial conditional plans used to decide the next action. A diagnostic model based on Bayesian Networks is used to update the current belief state after each action. The planner uses a simplified version of this model to update predicted belief states. We have tested the approach in the domain of troubleshooting heavy vehicles. Experiments show that a simplified model for planning improves performance when troubleshooting with limited time.

In Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pages 1306–1311. ISBN:978-390266146-3.DOI:10.3182/20090630-4-ES-2003.00212.

We consider computer assisted troubleshooting of complex systems, for example of a vehicle at a workshop. The objective is to identify the cause of a failure and repair a system at as low expected cost as possible. Three main challenges are: the need for disassembling the system during troubleshooting, the difficulty to verify that the system is fault free, and the dependencies in between components and observations. We present a method that can return a response anytime, which allows us to obtain the best result given the available time. The work is based on a case study of an auxiliary braking system of a modern truck. We highlight practical issues related to model building and troubleshooting in a real environment.

We consider a constrained optimization problem with mixed integer and real variables. It models optimal placement of communications relay nodes in the presence of obstacles. This problem is widely encountered, for instance, in robotics, where it is required to survey some target located in one point and convey the gathered information back to a base station located in another point. One or more unmanned aerial or ground vehicles (UAVs or UGVs) can be used for this purpose as communications relays. The decision variables are the number of unmanned vehicles (UVs) and the UV positions. The objective function is assumed to access the placement quality. We suggest one instance of such a function which is more suitable for accessing UAV placement. The constraints are determined by, firstly, a free line of sight requirement for every consecutive pair in the chain and, secondly, a limited communication range. Because of these requirements, our constrained optimization problem is a difficult multi-extremal problem for any fixed number of UVs. Moreover, the feasible set of real variables is typically disjoint. We present an approach that allows us to efficiently find a practically acceptable approximation to a global minimum in the problem of optimal placement of communications relay nodes. It is based on a spatial discretization with a subsequent reduction to a shortest path problem. The case of a restricted number of available UVs is also considered here. We introduce two label correcting algorithms which are able to take advantage of using some peculiarities of the resulting restricted shortest path problem. The algorithms produce a Pareto solution to the two-objective problem of minimizing the path cost and the number of hops. We justify their correctness. The presented results of numerical 3D experiments show that our algorithms are superior to the conventional Bellman-Ford algorithm tailored to solving this problem.

The study of frameworks and formalisms for reasoning about action and change [67, 58, 61, 65, 70, 3, 57] has been central to the knowledge representation field almost from the inception of Artificial Intelligence as a general field of research [52, 56]. The phrase âTemporal Action Logicsâ represents a class of logics for reasoning about action and change that evolved from Sandewallâs book on Features and Fluents [61] and owes much to this ambitious project. There are essentially three major parts to Sandewallâs work. He first developed a narrative-based logical framework for specifying agent behavior in terms of action scenarios. The logical framework is state-based and uses explicit time structures. He then developed a formal framework for assessing the correctness (soundness and completeness) of logics for reasoning about action and change relative to a set of well-defined intended conclusions, where reasoning problems were classified according to their ontological or epistemological characteristics. Finally, he proposed a number of logics defined semantically in terms of definitions of preferential entailment1 and assessed their correctness using his assessment framework.

Cooperation is a complex task that necessarily involves communication and reasoning about othersâ intentions and beliefs. Multi-agent communication languages aid designers of cooperating robots through standardized speech acts, sometimes including a formal semantics. But a more direct approach would be to have the robots plan both regular and communicative actions themselves. We show how two robots with heterogeneous capabilities can autonomously decide to cooperate when faced with a task that would otherwise be impossible. Request and inform speech acts are formulated in the same first-order logic of action and change as is used for regular actions. This is made possible by treating the contents of communicative actions as quoted formulas of the same language. The robot agents then use a natural deduction theorem prover to generate cooperative plans for an example scenario by reasoning directly with the axioms of the theory.

Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal KPL language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.

In Aspects of Natural Language Processing: Essays Dedicated to Leonard Bolc on the Occasion of His 75th Birthday, pages 43–58. In series: Lecture Notes in Computer Science #5070. Springer. ISBN:978-3-642-04734-3.DOI:10.1007/978-3-642-04735-0_2.find book at a swedish library/hitta boken i ett svenskt bibliotek:http://libris.kb.se/bib/11741557

Fuzzy logics are one of the most frequent approaches to model uncertainty and vagueness. In the case of fuzzy modeling, degrees of belief and disbelief sum up to 1, which causes problems in modeling the lack of knowledge and inconsistency. Therefore, so called paraconsistent intuitionistic fuzzy sets have been introduced, where the degrees of belief and disbelief are not required to sum up to 1. The situation when this sum is smaller than 1 reflects the lack of knowledge and its value greater than 1 models inconsistency. In many applications there is a strong need to guide and interpret fuzzy-like reasoning using qualitative approaches. To achieve this goal in the presence of uncertainty, lack of knowledge and inconsistency, we provide a framework for qualitative interpretation of the results of fuzzy-like reasoning by labeling numbers with words, like <em>true, false, inconsistent, unknown</em>, reflecting truth values of a suitable, usually finitely valued logical formalism.

Mathematical theory of voting and social choice has attracted much attention. In the general setting one can view social choice as a method of aggregating individual, often conflicting preferences and making a choice that is the best compromise. How preferences are expressed and what is the âbest compromiseâ varies and heavily depends on a particular situation. The method we propose in this paper depends on expressing individual preferences of voters and specifying properties of the resulting ranking by means of first-order formulas. Then, as a technical tool, we use methods of second-order quantifier elimination to analyze and compute results of voting. We show how to specify voting, how to compute resulting rankings and how to verify voting protocols.

We give the first ExpTime (optimal) tableau decision procedure for checking satisfiability of a knowledge base in the description logic ALC, not based on transformation that encodes ABoxes by nominals or terminology axioms. Our procedure can be implemented as an extension of the highly optimized tableau prover TGC [12] to obtain an efficient program for the mentioned satisfiability problem.

In Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change (NRAC). UTSePress. ISBN:978-0-9802840-7-2.

For logical artificial intelligence to be truly useful,its methods must scale to problems of realistic size.An interruptible algorithm enables a logical agentto act in a timely manner to the best of its knowledge,given its reasoning so far. This seems necessaryto avoid analysis paralysis, trying to thinkof every potentiality, however unlikely, beforehand.These considerations prompt us to look for alternativereasoning mechanisms for filtered circumscription,a nonmonotonic reasoning formalism usede.g. by Temporal Action Logic and Event Calculus.We generalize Ginsbergâs circumscriptive theoremprover and describe an interruptible theoremprover based on abduction that has been used tounify planning and reasoning in a logical agent architecture.

In Proceedings of the 1st International Conference on Computer Supported Education (CSEDU), pages 5–12. ISBN:978-989-8111-82-1.

Swedish engineering students conceptions of engineering is investigated by a large nation-wide study in ten Swedish higher education institutions. Based on data from surveys and interviews, categories and top-lists, a picture of students conceptions of engineering is presented. Students conceptions of engineering, are somewhat divergent, but dealing with problems and their solutions and creativity are identified as core concepts. The survey data is in general more varied and deals with somewhat different kinds of terms. When explicitly asking for five engineering terms, as in the survey, a broader picture arises including terms, or concepts, denoting how students think of engineering and work in a more personal way. For example, words like hard work, stressful, challenging, interesting, and fun are used. On the other hand, it seems like the interviewed students tried to give more general answers that were not always connected to their personal experiences. Knowledge on students conceptions of engineering is essential for practitioners in engineering education. By information on students conceptions, the teaching can approach students at their particular mindset of the engineering field. Program managers with responsibility for design of engineering programs would also benefit using information on students conceptions of engineering. Courses could be motivated and contextualized in order to connect with the students. Recruitment officers would also have an easier time marketing why people should chose the engineering track.

This paper investigates the possibility of augmenting an Unmanned Aerial Vehicle (UAV) navigation system with a passive video camera in order to cope with long-term GPS outages. The paper proposes a vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image. The vision-aided navigation system developed is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system. Due to the use of image-to-map registration for absolute position calculation, drift-free position performance depends on the structural characteristics of the terrain. Experimental evaluation of the approach based on offline flight data is provided. In addition the architecture proposed has been implemented on-board an experimental UAV helicopter platform and tested during vision-based autonomous flights.

The thesis has been developed as part of the requirements for a PhD degree at the Artificial Intelligence and Integrated Computer System division (AIICS) in the Department of Computer and Information Sciences at LinkÃ¶ping University.The work focuses on issues related to Unmanned Aerial Vehicle (UAV) navigation, in particular in the areas of guidance and vision-based autonomous flight in situations of short and long term GPS outage.The thesis is divided into two parts. The first part presents a helicopter simulator and a path following control mode developed and implemented on an experimental helicopter platform. The second part presents an approach to the problem of vision-based state estimation for autonomous aerial platforms which makes use of geo-referenced images for localization purposes. The problem of vision-based landing is also addressed with emphasis on fusion between inertial sensors and video camera using an artificial landing pad as reference pattern. In the last chapter, a solution to a vision-based ground object geo-location problem using a fixed-wing micro aerial vehicle platform is presented.The helicopter guidance and vision-based navigation methods developed in the thesis have been implemented and tested in real flight-tests using a Yamaha Rmax helicopter. Extensive experimental flight-test results are presented.

This Masterâs thesis describes an evaluation of the stream-based knowledge pro-cessing middleware framework DyKnow in multi-UAV traffic monitoring applica-tions performed at Saab Aerosystems. The purpose of DyKnow is âto providegeneric and well-structured software support for the processes involved in gen-erating state, object, and event abstractions about the environments of complexsystems.\" It does this by providing the concepts of streams, sources, computa-tional units (CUs), entity frames and chronicles.This evaluation is divided into three parts: A general quality evaluation ofDyKnow using the ISO 9126-1 quality model, a discussion of a series of questionsregarding the specific use and functionality of DyKnow and last, a performanceevaluation. To perform parts of this evaluation, a test application implementinga traffic monitoring scenario was developed using DyKnow and the Java AgentDEvelopment Framework (JADE).The quality evaluation shows that while DyKnow suffers on the usability side,the suitability, accuracy and interoperability were all given high marks.The results of the performance evaluation high-lights the factors that affect thememory and CPU requirements of DyKnow. It is shown that the most significantfactor in the demand placed on the CPU is the number of CUs and streams. Italso shows that DyKnow may suffer dataloss and severe slowdown if the CPU istoo heavily utilized. However, a reasonably sized DyKnow application, such as thescenario implemented in this report, should run without problems on systems atleast half as fast as the one used in the tests.

As robotic systems become more and more advanced the need to integrate existing deliberative functionalities such as chronicle recognition, motion planning, task planning, and execution monitoring increases. To integrate such functionalities into a coherent system it is necessary to reconcile the different formalisms used by the functionalities to represent information and knowledge about the world. To construct and integrate these representations and maintain a correlation between them and the environment it is necessary to extract information and knowledge from data collected by sensors. However, deliberative functionalities tend to assume symbolic and crisp knowledge about the current state of the world while the information extracted from sensors often is noisy and incomplete quantitative data on a much lower level of abstraction. There is a wide gap between the information about the world normally acquired through sensing and the information that is assumed to be available for reasoning about the world.As physical autonomous systems grow in scope and complexity, bridging the gap in an ad-hoc manner becomes impractical and inefficient. Instead a principled and systematic approach to closing the sensereasoning gap is needed. At the same time, a systematic solution has to be sufficiently flexible to accommodate a wide range of components with highly varying demands. We therefore introduce the concept of knowledge processing middleware for a principled and systematic software framework for bridging the gap between sensing and reasoning in a physical agent. A set of requirements that all such middleware should satisfy is also described.A stream-based knowledge processing middleware framework called DyKnow is then presented. Due to the need for incremental refinement of information at different levels of abstraction, computations and processes within the stream-based knowledge processing framework are modeled as active and sustained knowledge processes working on and producing streams. DyKnow supports the generation of partial and context dependent stream-based representations of past, current, and potential future states at many levels of abstraction in a timely manner.To show the versatility and utility of DyKnow two symbolic reasoning engines are integrated into Dy-Know. The first reasoning engine is a metric temporal logical progression engine. Its integration is made possible by extending DyKnow with a state generation mechanism to generate state sequences over which temporal logical formulas can be progressed. The second reasoning engine is a chronicle recognition engine for recognizing complex events such as traffic situations. The integration is facilitated by extending DyKnow with support for anchoring symbolic object identifiers to sensor data in order to collect information about physical objects using the available sensors. By integrating these reasoning engines into DyKnow, they can be used by any knowledge processing application. Each integration therefore extends the capability of DyKnow and increases its applicability.To show that DyKnow also has a potential for multi-agent knowledge processing, an extension is presented which allows agents to federate parts of their local DyKnow instances to share information and knowledge.Finally, it is shown how DyKnow provides support for the functionalities on the different levels in the JDL Data Fusion Model, which is the de facto standard functional model for fusion applications. The focus is not on individual fusion techniques, but rather on an infrastructure that permits the use of many different fusion techniques in a unified framework.The main conclusion of this thesis is that the DyKnow knowledge processing middleware framework provides appropriate support for bridging the sense-reasoning gap in a physical agent. This conclusion is drawn from the fact that DyKnow has successfully been used to integrate different reasoning engines into complex unmanned aerial vehicle (UAV) applications and that it satisfies all the stated requirements for knowledge processing middleware to a significant degree.

In Proceedings of the IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS), pages 1606–1611. IEEE conference proceedings. ISBN:978-1-4244-2057-5.DOI:10.1109/IROS.2008.4650986.

In the context of stereovision SLAM, we propose a way to enrich the landmark models. Vision-based SLAM approaches usually rely on interest points associated to a point in the Cartesian space: by adjoining oriented planar patches (if they are present in the environment), we augment the landmark description with an oriented frame. Thanks to this additional information, the robot pose is fully observable with the perception of a single landmark, and the knowledge of the patches orientation helps the matching of landmarks. The paper depicts the chosen landmark model, the way to extract and match them, and presents some SLAM results obtained with such landmarks.

In Proceedings of the 26th International Congress of the Aeronautical Sciences (ICAS). Optimage Ltd.. ISBN:ISBN 0-9533991-9-2.

<em>To expand the operative area for surveillance UAV, we propose the use of a relay UAV. The relay UAV is used as an intermediary node in a communication network: the surveillance UAV transmits data to the relay UAV, which sends it back to a ground station. In this exploratory report, we calculate the route for a relay UAV, to ensure communication at certain time points, given the route of the surveillance UAV. The results presented here are preliminary and may be considered </em><em>a first iteration of ideas and </em> <em>methods. </em>

We provide a technique to reconstruct an object configuration that has been described on site by only using intrinsic and relative frames of reference into an absolute frame of reference, as seen from the survey perspective.

The problem of automatically selecting simulation models for autonomous agents depending on their current intentions and beliefs is considered in this paper. The intended use of the models is for prediction, filtering, planning and other types of reasoning that can be performed with Simulation models. The parameters and model fragments of the resulting model are selected by formulating and solving a hybrid constrained optimization problem that captures the intuition of the preferred model when relevance information about the elements of the world being modelled is taken into consideration. A specialized version of the original optimization problem is developed that makes it possible to solve the continuous subproblem analytically in linear time. A practical model selection problem is discussed where the aim is to select suitable parameters and models for tracking dynamic objects. Experiments with randomly generated problem instances indicate that a hillclimbing search approach might be both efficient and provides reasonably good solutions compared to simulated annealing and hillclimbing with random restarts.

This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

In Proceedings of the International Workshop on the Principles of Diagnosis.

When troubleshooting malfunctioning technical equipment, the task is to locate faults and make repairsuntil the equipment functions properly again. The AO* algorithm can be used to find troubleshootingstrategies that are optimal in the sense that the expected cost of repair is minimal. We have adaptedthe AO* algorithm for troubleshooting in the automotive domain with limited time. We propose a newheuristic based on entropy. By using this heuristic, near-optimal strategies can be found within a fixedtime limit. This is shown in empirical studies on a fuel injection system of a truck. In these results, theAO* algorithm using the new heuristic, performs better than other troubleshooting algorithms.

We propose a troubleshooting algorithm that can troubleshoot systems with dependent action costs. When actions are performed they may change the way the system is decomposed and affect the cost of future actions. We present a way to model this by extending the traditional troubleshooting model with an additional state that describes which parts of the system that are decomposed. The proposed troubleshooting algorithm searches an AND/OR graph with the aim of finding the repair plan that minimizes the expected cost of repair. We present the heuristics needed to speed up the search and make it competitive with other troubleshooting algorithms. Finally, the performance of the algorithm is evaluated on a probabilistic model of a fuel injection system of a truck.We show that the expected cost of repair can be reduced when compared with an algorithm from previous literature.

The concept of a 'publication' no longer applies only to printed works, information technology has extended its application to several other types of works. This article describes a facility called the Common Knowledge Library that publishes modules of formally structured information representing facts and knowledge of various kinds. Publications of this new type have some characteristics in common with databases, and others in common with software modules, however, they also share some important characteristics with traditional publications. A framework for citation of previous work is important in order to provide an incentive for contributors of such modules. Peer review - the traditional method of quality assurance for scientific articles - must also be applied, although in a modified form, for fact and knowledge modules. The construction of the Common Knowledge Library is a cumulative process, new contributions are obtained by interpreting the contents of existing knowledge sources on the Internet, and the existing contents of the Library are an important resource for that interpretation process.

The aim of this paper is to explore the possibility of using geo-referenced satellite or aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. A vision based navigation system which combines inertial sensors, visual odometer and registration of a UAV on-board video to a given geo-referenced aerial image has been developed and tested on real flight-test data. The experimental results show that it is possible to extract useful position information from aerial imagery even when the UAV is flying at low altitude. It is shown that such information can be used in an automated way to compensate the drift of the UAV state estimation which occurs when only inertial sensors and visual odometer are used.

In Proceedings of the AIAA Guidance, Navigation, and Control Conference (GNC). AIAA. ISBN:978-1-56347-945-8.

This paper presents a method for high accuracy ground target localization using a Micro Aerial Vehicle (MAV) equipped with a video camera sensor. The proposed method is based on a satellite or aerial image registration technique. The target geo-location is calculated by registering the ground target image taken from an on-board video camera with a geo- referenced satellite image. This method does not require accurate knowledge of the aircraft position and attitude, therefore it is especially suitable for MAV platforms which do not have the capability to carry accurate sensors due to their limited payload weight and power resources. The paper presents results of a ground target geo-location experiment based on an image registration technique. The platform used is a MAV prototype which won the 3rd US-European Micro Aerial Vehicle Competition (MAV07). In the experiment a ground object was localized with an accuracy of 2.3 meters from a ight altitude of 70 meters.

Recent advances in the field of Unmanned Aerial Vehicles (UAVs) make flying robots suitable platforms for carrying sensors and computer systems capable of performing advanced tasks. This paper presents a technique which allows detecting humans at a high frame rate on standard hardware onboard an autonomous UAV in a real-world outdoor environment using thermal and color imagery. Detected human positions are geolocated and a map of points of interest is built. Such a saliency map can, for example, be used to plan medical supply delivery during a disaster relief effort. The technique has been implemented and tested on-board the UAVTech<sup>1</sup> autonomous unmanned helicopter platform as a part of a complete autonomous mission. The results of flight- tests are presented and performance and limitations of the technique are discussed.

In the paper we present a technique for eliminating quantifiers of arbitrary order, in particular of first-order. Such a uniform treatment of the elimination problem has been problematic up to now, since techniques for eliminating first-order quantifiers do not scale up to higher-order contexts and those for eliminating higher-order quantifiers are usually based on a form of monotonicity w.r.t implication (set inclusion) and are not applicable to the first-order case. We make a shift to arbitrary relations \"ordering\" the underlying universe. This allows us to incorporate background theories into higher-order quantifier elimination methods which, up to now, has not been achieved. The technique we propose subsumes many other results, including the Ackermann's lemma and various forms of fixpoint approaches when the \"ordering\" relations are interpreted as implication and reveals the common principle behind these approaches.

A substantial knowledgebase is an important part of many A.I. applications as well as (arguably) in any system that is claimed to implement broad-range intelligence. Although this has been an accepted view in our field since very long, little progress has been made towards the establishment of large and sharable knowledgebases. Both basic research projects and applications projects have found it necessary to construct special-purpose knowledgebases for their respective needs. This is obviously a problem: it would save work and speed up progress if the construction of a broadly sharable and broadly useful knowledgebase could be a joint undertaking for the field. In this article I wish to discuss the possibilities and the obstacles in this respect. I shall argue that the field of Knowledge Representation needs to adopt a new and very different paradigm in order for progress to be made, so that besides working as usual on logical foundations and on algorithms, we should also devote substantial efforts to the systematic preparation of knowledgebase contents.

The topic of preference modeling has recently attracted the interest of a number of sub-disciplines in artificial intelligence such as the nonmonotonic reasoning and action and change communities. The approach in these communities focuses on qualitative preferences and preference models which provide more natural representations from a~commonsense perspective. In this paper, we show how generalized circumscription can be used as a highly expressive framework for qualitative preference modeling. Generalized circumscription proposed by Lifschitz allows for predicates (and thus formulas) to be minimized relative to arbitrary pre-orders (reflexive and transitive). Although it has received little attention, we show how it may be used to model and reason about elaborate qualitative preference relations. One of the perceived weaknesses with any type of circumscription is the 2nd-order nature of the representation. The paper shows how a large variety of preference theories represented using generalized circumscription can in fact be reduced to logically equivalent first-order theories in a constructive way. Finally, we also show how preference relations represented using general circumscription can be extended with cardinality constraints and when these extensions can also be reduced to logically equivalent first-order theories.

In recent years there has been an increasing use of logical methods and significant new developments have been spawned in several areas of computer science, ranging from artificial intelligence and software engineering to agent-based systems and the semantic web. In the investigation and application of logical methods there is a tension between: * the need for a representational language strong enough to express domain knowledge of a particular application, and the need for a logical formalism general enough to unify several reasoning facilities relevant to the application, on the one hand, and * the need to enable computationally feasible reasoning facilities, on the other hand. Second-order logics are very expressive and allow us to represent domain knowledge with ease, but there is a high price to pay for the expressiveness. Most second-order logics are incomplete and highly undecidable. It is the quantifiers which bind relation symbols that make second-order logics computationally unfriendly. It is therefore desirable to eliminate these second-order quantifiers, when this is mathematically possible; and often it is. If second-order quantifiers are eliminable we want to know under which conditions, we want to understand the principles and we want to develop methods for second-order quantifier elimination. This book provides the first comprehensive, systematic and uniform account of the state-of-the-art of second-order quantifier elimination in classical and non-classical logics. It covers the foundations, it discusses in detail existing second-order quantifier elimination methods, and it presents numerous examples of applications and non-standard uses in different areas. These include: * classical and non-classical logics, * correspondence and duality theory, * knowledge representation and description logics, * commonsense reasoning and approximate reasoning, * relational and deductive databases, and * complexity theory. The book is intended for anyone interested in the theory and application of logics in computer science and artificial intelligence.

Gilles Kahn was one of the most influential figures in the development of computer science and information technology, not only in Europe but throughout the world. This volume of articles by several leading computer scientists serves as a fitting memorial to Kahn's achievements and reflects the broad range of subjects to which he contributed through his scientific research and his work at INRIA, the French National Institute for Research in Computer Science and Control. The editors also reflect upon the future of computing: how it will develop as a subject in itself and how it will affect other disciplines, from biology and medical informatics, to web and networks in general. Its breadth of coverage, topicality, originality and depth of contribution, make this book a stimulating read for all those interested in the future development of information technology.

We present a process for reconstructing object configurations described by a set of spatial constraints of the form (A northeast B) into a two-dimensional grid. The reconstruction process is cognitively easy for a person to fulfill and guides the user to avoid typical mistakes. For underspecified object configuration descriptions we suggest a strategy to handle coarse object relationships by representing a coarse object in a way that all disjunctive basic relationships that the coarse relationship consists of are represented within one reconstruction.

The newly appeared Handbook of Knowledge Representation is an impressive piece of work. Its three editors and its forty-five contributors have produced twenty-five concise, textbook-style chapters that introduce most of the major aspects of the science of knowledge representation. Reading this book is a very positive experience: it demonstrates the breadth, the depth and the coherence that our field has achieved by now.

In Proceedings of the 11th International Conference on Information Fusion (FUSION). IEEE conference proceedings. ISBN:978-3-8007-3092-6.

As unmanned aerial vehicle (UAV) applications become more complex and versatile there is an increasing need to allow multiple UAVs to cooperate to solve problems which are beyond the capability of each individual UAV. To provide more complete and accurate information about the environment we present a DyKnow federation framework for information integration in multi-node networks of UAVs. A federation is created and maintained using a multiagent delegation framework and allows UAVs to share local information as well as process information from other UAVs as if it were local using the DyKnow knowledge processing middleware framework. The work is presented in the context of a multi UAV traffic monitoring scenario.

Inhabiting the complex and dynamic environments of modern computer games with autonomous agents capable of intelligent timely behaviour is a significant research challenge. We illustrate this using Our own attempts to build a practical agent architecture on it logicist foundation. In the ANDI-Land adventure game concept players solve puzzles by eliciting information from computer characters through natural language question answering. While numerous challenges immediately presented themselves, they took on a form of concrete and accessible problems to solve, and we present some of our initial solutions. We conclude that games, due to their demand for human-like computer characters with robust and independent operation in large simulated worlds, might serve as excellent test beds for research towards artificial general intelligence.

In Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 528–534. AAAI Press. ISBN:978-1-57735-384-3.

Agents plan to achieve and maintain goals. Maintenance that requires continuous action excludes the representation of plans as finite sequences of actions. If there is no upper bound on the number of actions, a simple list of actions would be infinitely long. Instead, a compact representation requires some form of looping construct. We look at a specific temporally extended maintenance goal, multiple target video surveillance, and formalize it in Temporal Action Logic. The logic's representation of time as the natural numbers suggests using mathematical induction to deductively plan to satisfy temporally extended goals. Such planning makes use of a sound and useful, but incomplete, induction rule that compactly represents the solution as a recursive fixpoint formula. Two heuristic rules overcome the problem of identifying a sufficiently strong induction hypothesis and enable an automated solution to the surveillance problem that satisfies the goal indefinitely.

In Martin Hulse and Manfred Hild, editors, IROS Workshop on Current Software Frameworks in Cognitive Robotics Integrating Different Computational Paradigms. Note: No proceedings, but CD

Developing autonomous agents displaying rational and goal-directed behavior in a dynamic physical environment requires the integration of both sensing and reasoning components. Due to the different characteristics of these components there is a gap between sensing and reasoning. We believe that this gap can not be bridged in a single step with a single technique. Instead, it requires a more general approach to integrating components on many different levels of abstraction and organizing them in a structured and principled manner. In this paper we propose knowledge processing middleware as a systematic approach for organizing such processing. Desirable properties of such middleware are presented and motivated. We then go on to argue that a declarative streambased system is appropriate to provide the desired functionality. Finally, DyKnow, a concrete example of stream-based knowledge processing middleware that can be used to bridge the sense-reasoning gap, is presented. Different types of knowledge processes and components of the middleware are described and motivated in the context of a UAV traffic monitoring application.

In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press. ISBN:978-1-57735-386-7, 978-1-57735-387-4.

As no plan can cover all possible contingencies, the ability to detect failures during plan execution is crucial to the robustness of any autonomous system operating in a dynamic and uncertain environment. In this paper we present a general planning and execution monitoring system where formulas in an expressive temporal logic specify the desired behavior of a system and its environment. A unified domain description for planning and monitoring provides a solid shared declarative semantics permitting the monitoring of both global and operator-specific conditions. During plan execution, an execution monitor subsystem detects violations of monitor formulas in a timely manner using a progression algorithm on incrementally generated partial logical models. The system has been integrated on a fully deployed autonomous unmanned aircraft system. Extensive empirical testing has been performed using a combination of actual flight tests and hardware-in-the-loop simulations in a number of different mission scenarios.

We present a novel algorithm for visibility approximation that is substantially faster than ray casting based algorithms. The algorithm does not require extensive preprocessing or specialized hardware as most other algorithms do. We test this algorithm in several settings: rural, mountainous and urban areas, with different view ranges and grid cell sizes. By changing the size of the grid cells that the algorithm uses, it is possible to tailor the algorithm between speed and accuracy.

This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. The second one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle models are computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

In Proceedings of the 47th IEEE Conference on Decision and Control, pages 1066–1072. In series: IEEE Conference on Decision and Control. Proceedings #??. IEEE. ISBN:978-1-4244-3124-3, 978-1-4244-3123-6.DOI:10.1109/CDC.2008.4738793.

Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper proposes a method to reduce the computational burden by only running the tests that are currently needed, and dynamically starting new tests when the need changes. A main contribution is a method to select tests such that the computational burden is reduced while maintaining the isolation performance of the diagnostic system. Key components in the approach are the test selection algorithm, the test initialization procedures, and a knowledge processing framework that supports the functionality needed. The approach is exemplified on a relatively small dynamical system, which still illustrates the complexity and possible computational gain with the proposed approach.

Rough set approximations of Pawlak [15] are sometimes generalized by using similarities between objects rather than elementary sets. In practical applications, both knowledge about properties of objects and knowledge of similarity between objects can be incomplete and inconsistent. The aim of this paper is to define set approximations when all sets, and their approximations, as well as similarity relations are four-valued. A set is four-valued in the sense that its membership function can have one of the four logical values: unknown (<strong>u</strong>), false (<strong>f</strong>), inconsistent (<strong>i</strong>), or true (<strong>t</strong>). To this end, a new implication operator and set-theoretical operations on four-valued sets, such as set containment, are introduced. Several properties of lower and upper approximations of four-valued sets are also presented.

This paper presents a language for defining four-valued rough sets and to reason about them. Our framework brings together two major fields: rough sets and paraconsistent logic programming. On the one hand it provides a paraconsistent approach, based on four-valued rough sets, for integrating knowledge from different sources and reasoning in the presence of inconsistencies. On the other hand, it also caters for a specific type of uncertainty that originates from the fact that an agent may perceive different objects of the universe as being indiscernible. This paper extends the ideas presented in [9]. Our language allows the user to define similarity relations and use the approximations induced by them in the definition of other four-valued sets. A positive aspect is that it allows users to tune the level of uncertainty or the source of uncertainty that best suits applications.

This contribution aims at unifying two recent trends in applied particle filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) applications, utilizing the FastSLAM algorithm. Thesecond one is the implications of the marginalized particle filter (MPF) or the Rao-Blackwellized particle filter (RBPF) in positioning and tracking applications. Using the standard FastSLAM algorithm, only low-dimensional vehicle modelsare computationally feasible. In this work, an algorithm is introduced which merges FastSLAM and MPF, and the result is an algorithm for SLAM applications, where state vectors of higher dimensions can be used. Results using experimental data from a UAV (helicopter) are presented. The algorithmfuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the SLAM problem, i.e., enable navigation over a long period of time.

In Proceedings of the 19th International Workshop on Principles of Diagnosis (DX).

Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on the test results. This paper describes FlexDx, a reconfigurable diagnosis framework which reduces the computational burden by only running the tests that are currently needed. The method selects tests such that the isolation performance of the diagnostic system is maintained. Special attention is given to the practical issues introduced by a reconfigurable diagnosis framework such as FlexDx. For example, tests are added and removed dynamically, tests are partially performed on historic data, and synchronous and asynchronous processing are combined. To handle these issues FlexDx uses DyKnow, a stream-based knowledge processing middleware framework. The approach is exemplified on a relatively small dynamical system, which still illustrates the computational gain with the proposed approach.

Welcome to the latest volume of AI Game Programming Wisdom! AI Game Programming Wisdom 4 includes a collection of more than 50 new articles featuring cutting-edge techniques, algorithms, and architectures written by industry professionals for use in commercial game development. Organized into 7 sections, this comprehensive volume explores every important aspect of AI programming to help you develop and expand your own personal AI toolbox. You'll find ready-to-use ideas, algorithms, and code in all key AI areas including general wisdom, scripting and dialogue, movement and pathfinding, architecture, tactics and planning, genre specific, and learning and adaptation. New to this volume are articles on recent advances in realistic agent, squad, and vehicle movement, as well as dynamically changing terrain, as exemplified in such popular games as Company of Heroes.You'll also find information on planning as a key game architecture, as well as important new advances in learning algorithms and player modeling. AI Game Programming Wisdom 4 features coverage of multiprocessor architectures, Bayesian networks, planning architectures, conversational AI, reinforcement learning, and player modeling.These valuable and innovative insights and issues offer the possibility of new game AI experiences and will undoubtedly contribute to taking the games of tomorrow to the next level.

Recent advances in the field of micro unmanned aerial vehicles (MAVs) make flying robots of small dimensions suitable platforms for performing advanced indoor missions. In order to achieve autonomous indoor flight a pose estimation technique is necessary. This paper presents a complete system which incorporates a vision-based pose estimation method to allow a MAV to navigate in indoor environments in cooperation with a ground robot. The pose estimation technique uses a lightweight light emitting diode (LED) cube structure as a pattern attached to a MAV. The pattern is observed by a ground robot's camera which provides the flying robot with the estimate of its pose. The system is not confined to a single location and allows for cooperative exploration of unknown environments. It is suitable for performing missions of a search and rescue nature where a MAV extends the range of sensors of the ground robot. The performance of the pose estimation technique and the complete system is presented and experimental flights of a vertical take-off and landing (VTOL) MAV are described.

Developing autonomous agents displaying rational and goal-directed behavior in a dynamic physical environment requires the integration of a great number of separate deliberative and reactive functionalities. This integration must be built on top of a solid foundation of data, information and knowledge having numerous origins, including quantitative sensors and qualitative knowledge databases. Processing is generally required on many levels of abstraction and includes refinement and fusion of noisy sensor data and symbolic reasoning. We propose the use of knowledge processing middleware as a systematic approach for organizing such processing. Desirable properties of such middleware are presented and motivated. We then argue that a declarative stream-based system is appropriate to provide the desired functionality. Different types of knowledge processes and components of the middleware are described and motivated in the context of a UAV traffic monitoring application. Finally DyKnow, a concrete example of stream-based knowledge processing middleware, is briefly described.

We study the problem of positioning unmanned aerial vehicles (UAVs) to maintain an unobstructed flow of communication from a surveying UAV to some base station through the use of multiple relay UAVs. This problem can be modeled as a hopconstrained shortest path problem in a large visibility graph. We propose a dual ascent method for solving this problem, optionally within a branch-and-bound framework. Computational tests show that realistic problems can be solved in a reasonably short time, and that the proposed method is faster than the classical dynamic programming approach.

It is often beneficial for an autonomous agent that operates in a complex environment to make use of different types of mathematical models to keep track of unobservable parts of the world or to perform prediction, planning and other types of reasoning. Since a model is always a simplification of something else, there always exists a tradeoff between the modelâs accuracy and feasibility when it is used within a certain application due to the limited available computational resources. Currently, this tradeoff is to a large extent balanced by humans for model construction in general and for autonomous agents in particular. This thesis investigates different solutions where such agents are more responsible for balancing the tradeoff for models themselves in the context of interleaved task planning and plan execution. The necessary components for an autonomous agent that performs its abstractions and constructs planning models dynamically during task planning and execution are investigated and a method called DARE is developed that is a template for handling the possible situations that can occur such as the rise of unsuitable abstractions and need for dynamic construction of abstraction levels. Implementations of DARE are presented in two case studies where both a fully and partially observable stochastic domain are used, motivated by research with Unmanned Aircraft Systems. The case studies also demonstrate possible ways to perform dynamic abstraction and problem model construction in practice.

Two types of automatic fitting procedures for EPR spectra of disordered systems have been developed, one based on matrix diagonalisation of a general spin Hamiltonian, the other on 2<sup>nd</sup> order perturbation theory. The first program is based on a previous Fortran code complemented with a newly written interface in Java to provide user-friendly in- and output. The second is intended for the special case of free radicals with several relatively weakly interacting nuclei, in which case the general method becomes slow. A least squaresâ fitting procedure utilizing analytical or numerical derivatives of the theoretically calculated spectrum with respect to the g-and hyperfine structure (hfs) tensors was used to refine those parameters in both cases. âRigid limitâ ESR spectra from radicals in organic matrices and in polymers, previously studied experimentally at low temperature, were analysed by both methods. Fluoro-carbon anion radicals could be simulated, quite accurately with the exact method, whereas automatic fitting on e.g. the c-C<sub>4</sub>F<sub>8</sub><sup>-</sup> anion radical is only feasible with the 2<sup>nd</sup> order approximative treatment. Initial values for the <sup>19</sup>F hfs tensors estimated by DFT calculations were quite close to the final. For neutral radicals of the type XCF<sub>2</sub>CF<sub>2</sub>â¢ the refinement of the hfs tensors by the exact method worked better than the approximate. The reasons are discussed. The ability of the fitting procedures to recover the correct magnetic parameters of disordered systems was investigated by fittings to synthetic spectra with known hfs tensors. The exact and the approximate methods are concluded to be complementary, one being general, but limited to relatively small systems, the other being a special treatment, suited for S=Â½ systems with several moderately large hfs.

One reason Qualitative Spatial Reasoning (QSR) is becoming increasingly important to Artificial Intelligence (AI) is the need for a smooth âhuman-likeâ communication between autonomous agents and people. The selected, yet general, task motivating the work presented here is the scenario of an object configuration that has to be described by an observer on the ground using only relational object positions. The description provided should enable a second agent to create a map-like picture of the described configuration in order to recognize the configuration on a representation from the survey perspective, for instance on a geographic map or in the landscape itself while observing it from an aerial vehicle. Either agent might be an autonomous system or a person. Therefore, the particular focus of this work lies on the necessity to develop description and reconstruction methods that are cognitively easy to apply for a person.This thesis presents the representation scheme QuaDRO (Qualitative Description and Reconstruction of Object configurations). Its main contributions are a specification and qualitative classification of information available from different local viewpoints into nine qualitative equivalence classes. This classification allows the preservation of information needed for reconstruction nto a global frame of reference. The reconstruction takes place in an underlying qualitative grid with adjustable granularity. A novel approach for representing objects of eight different orientations by two different frames of reference is used. A substantial contribution to alleviate the reconstruction process is that new objects can be inserted anywhere within the reconstruction without the need for backtracking or rereconstructing. In addition, an approach to reconstruct configurations from underspecified descriptions using conceptual neighbourhood-based reasoning and coarse object relations is presented.

The flight dynamics of the Yamaha RMAX unmanned helicopter has been investigated, and mapped into a six degrees of freedom mathematical model. The model has been obtained by a combined black-box system identification technique and a classic model-based parameter identification approach. In particular, the closed-loop behaviour of the built-in attitude control system has been studied, to support the decision whether to keep it as inner stabilization loop or to develop an own stability augmentation system. The flight test method and the test instrumentation are described in detail; some samples of the flight test data are compared to the model outputs as validation, and an overall assessment of the built-in stabilization system is supplied.

In Proceedings of the 5th International Central and Eastern European Conference on Multi-Agent Systems (CEEMAS), pages 277–287. In series: Lecture Notes in Artificial Intelligence #4696. Springer Berlin/Heidelberg. ISBN:9783540752530.DOI:10.1007/978-3-540-75254-7_28.

This paper focuses on modelling perception and vague concepts in the context of multiagent Bgi (<em>Beliefs, Goals</em> and <em>Intentions</em>) systems. The starting point is the multimodal formalization of such systems. Then we make a shift from Kripke structures to similarity structures, allowing us to model perception and vagueness in an uniform way, âcompatibleâ with the multimodal approach. As a result we introduce and discuss <em>approximate B</em> <em>gi</em> <em>systems</em>, which can also be viewed as a way to implement multimodal specifications of Bgi systems in the context of perception.

Building a spatially consistent model is a key functionality to endow a mobile robot with autonomy. Without an initial map or an absolute localization means, it requires to concurrently solve the localization and mapping problems. For this purpose, vision is a powerful sensor, because it provides data from which stable features can be extracted and matched as the robot moves. But it does not directly provide 3D information, which is a difficulty for estimating the geometry of the environment. This article presents two approaches to the SLAM problem using vision: one with stereovision, and one with monocular images. Both approaches rely on a robust interest point matching algorithm that works in very diverse environments. The stereovision based approach is a classic SLAM implementation, whereas the monocular approach introduces a new way to initialize landmarks. Both approaches are analyzed and compared with extensive experimental results, with a rover and a blimp.

One of the most successful methods for planning in large partially observable stochastic domains is depth-limited forward search from the current belief state together with a utility estimation. However, when the environment is continuous and the number of possible actions is practically infinite, then abstractions have to be made before any forward search planning can be performed. The paper presents a method to dynamically generate such planning problem abstractions for a domain that is inspired by our research with unmanned aerial vehicles (UAVs). The planning problems are created by first stating the selection of points to fly to as an optimization problem. When the points have been selected, a set of possible paths between them are then created with a pathplanner and then forward search in the belief state space is applied. The method has been implemented and tested in simulation and the experiments show the importance of modelling both the dynamics of the environment and the limited computational resources of the architecture when searching for suitable parameters in the planning problem formulation procedure.

This paper focuses on approximate reasoning based on the use of similarity spaces. Similarity spaces and the approximated relations induced by them are a generalization of the rough set-based approximations of Pawlak [17, 18]. Similarity spaces are used to define neighborhoods around individuals and these in turn are used to define approximate sets and relations. In any of the approaches, one would like to embed such relations in an appropriate logic which can be used as a reasoning engine for specific applications with specific constraints. We propose a framework which permits a formal study of the relationship between approximate relations, similarity spaces and three-valued logics. Using ideas from correspondence theory for modal logics and constraints on an accessibility relation, we develop an analogous framework for three-valued logics and constraints on similarity relations. In this manner, we can provide a tool which helps in determining the proper three-valued logical reasoning engine to use for different classes of approximate relations generated via specific types of similarity spaces. Additionally, by choosing a three-valued logic first, the framework determines what constraints would be required on a similarity relation and the approximate relations induced by it. Such information would guide the generation of approximate relations for specific applications.

In 2002, LinkÃ¶ping University Electronic Press began development of a journal article reviewing support system (JARSS) as a tool for Editors of electronic journals. The system-s database contains submitted articles, abstracts and other secondary information, reviews, and e-mail communication for the purpose of submission, reviewing and final acceptance. The interface maintains a log of all events pertaining to the article and the associated status changes. The successive status options of an article (received, under review, conditional accept, etc.) correspond to the editorial workflow. JARRS has been used since its inception to run the Artificial Intelligence Journal (AIJ), an Elsevier publication. In essence a service is offered to take care of the technical aspects of journal publication, allowing editors more time to solicit papers of high quality.

The multi-agent system paradigm has proven to be a useful means of abstraction when considering distributed systems with interacting components. It is often the case that each component may be viewed as an intelligent agent with specific and often limited perceptual capabilities. It is also the case that these agent components may be used as information sources and such sources may be aggregated to provide global information about particular states, situations or activities in the embedding environment. This paper investigates a framework for information fusion based on the use of generalizations of rough set theory and the use of dynamic logic as a basis for aggregating similarity relations among objects where the similarity relations represent individual agents perceptual capabilities or limitations. As an added benefit, it is shown how this idea may also be integrated into description logics.

The concept of delegation is central to an understanding of the interactions between agents in cooperative agent problem-solving contexts. In fact, the concept of delegation offers a means for studying the formal connections between mixed-initiative problem-solving, adjustable autonomy and cooperative agent goal achievement. In this paper, we present an exploratory study of the delegation concept grounded in the context of a relatively complex multi-platform Unmanned Aerial Vehicle (UAV) catastrophe assistance scenario, where UAVs must cooperatively scan a geographic region for injured persons. We first present the scenario as a case study, showing how it is instantiated with actual UAV platforms and what a real mission implies in terms of pragmatics. We then take a step back and present a formal theory of delegation based on the use of 2APL and KARO. We then return to the scenario and use the new theory of delegation to formally specify many of the communicative interactions related to delegation used in achieving the goal of cooperative UAV scanning. The development of theory and its empirical evaluation is integrated from the start in order to ensure that the gap between this evolving theory of delegation and its actual use remains closely synchronized as the research progresses. The results presented here may be considered a first iteration of the theory and ideas.

In Proceedings of the 20th Australian Joint Conference on Artificial Intelligence (AI). Springer Berlin/Heidelberg. ISBN:978-3-540-76926-2.

The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. The UAVTech Lab, is pursuing a long term research endeavour related to the development of future aviation systems which try and push the envelope in terms of using and integrating high-level deliberative or AI functionality with traditional reactive and control components in autonomous UAV systems. In order to carry on such research, one requires challenging mission scenarios which force such integration and development. In this paper, one of these challenging emergency services mission scenarios is presented. It involves search and rescue for injured civilians by UAVs. In leg I of the mission, UAVs scan designated areas and try to identify injured civilians. In leg II of the mission, an attempt is made to deliver medical and other supplies to identified victims. We show how far we have come in implementing and executing such a challenging mission in realistic urban scenarios.

We present a system to estimate the altitude and motion of an aerial vehicle using a stereo visual system. The system has been initially tested on a ground robot and the novelty lays on its application and robustness validation in an UAV, where vibrations and rapid environmental changes take place. The two main functionalities are height estimation and visual odometry. The system first detects and tracks salient points in the scene. Depth to the plane containing the features is calculated matching features between left and right images then using the disparity principle. Motion is recovered tracking pixels from one frame to the next one finding its visual displacement and resolving camera rotation and translation by a least-square method. We present results from different experimental trials on the two platforms comparing and discussing the results regarding the trajectories calculated by the visual odometry and the onboard helicopter state estimation.

An implemented system for achieving high level situation awareness about traffic situations in an urban area is described. It takes as input sequences of color and thermal images which are used to construct and maintain qualitative object structures and to recognize the traffic behavior of the tracked vehicles in real time. The system is tested both in simulation and on data collected during test flights. To facilitate the signal to symbol transformation and the easy integration of the streams of data from the sensors with the GIS and the chronicle recognition system, DyKnow, a stream-based knowledge processing middleware, is used. It handles the processing of streams, including the temporal aspects of merging and synchronizing streams, and provides suitable abstractions to allow high level reasoning and narrow the sense reasoning gap.

Annotation The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume VI of the Transactions on Rough Sets (TRS) commemorates the life and work of Zdzislaw Pawlak (1926-2006). His legacy is rich and varied. Prof. Pawlak's research contributions have had far-reaching implications inasmuch as his works are fundamental in establishing new perspectives for scientific research in a wide spectrum of fields. This volume of the TRS presents papers that reflect the profound influence of a number of research initiatives by Professor Pawlak. In particular, this volume introduces a number of new advances in the foundations and applications of artificial intelligence, engineering, logic, mathematics, and science. These advances have significant implications in a number of research areas such as the foundations of rough sets, approximate reasoning, bioinformatics, computational intelligence, cognitive science, data mining, information systems, intelligent systems, machine intelligence, and security.

To achieve complex missions an autonomous unmanned aerial vehicle (UAV) operating in dynamic environments must have and maintain situational awareness. This can be achieved by continually gathering information from many sources, selecting the relevant information for current tasks, and deriving models about the environment and the UAV itself. It is often the case models suitable for traditional control, are not sufficient for deliberation. The need for more abstract models creates a sense-reasoning gap. This paper presents DyKnow, a knowledge processing middleware framework, and shows how it supports bridging the gap in a concrete UAV traffic monitoring application. In the example, sequences of color and thermal images are used to construct and maintain qualitative object structures. They model the parts of the environment necessary to recognize traffic behavior of tracked vehicles in real-time. The system has been implemented and tested in simulation and on data collected during flight tests.

Temporal Action Logic is a well established logical formalism for reasoning about action and change using an explicit time representation that makes it suitable for applications that involve complex temporal reasoning. We take advantage of constraint satisfaction technology to facilitate such reasoning through temporal constraint networks. Extensions are introduced that make generation of action sequences possible, thus paving the road for interesting applications in deductive planning. The extended formalism is encoded as a logic program that is able to realize a least commitment strategy that generates partial order plans in the context of both qualitative and quantitative temporal constraints.

A constraint solver based on concurrent search and propagation provides a well-defined component model for propagators by enforcing a strict two-level architecture. This makes it straightforward for third parties to invent, implement and deploy new kinds of propagators. The most critical components of such solvers are the constraint stores through which propagators communicate with each other. Introducing stores supporting new kinds of stored constraints can potentially increase the solving power by several orders of magnitude. This thesis presents a theoretical framework for designing stores achieving this without loss of propagator interoperability.

This thesis was written during the WITAS UAV Project where one of the goals has been the development of a software/hardware architecture for an unmanned autonomous helicopter, in addition to autonomous functionalities required for complex mission scenarios. The algorithms developed here have been tested on an unmanned helicopter platform developed by Yamaha Motor Company called the RMAX. The character of the thesis is primarily experimental and it should be viewed as developing navigational functionality to support autonomous flight during complex real world mission scenarios. This task is multidisciplinary since it requires competence in aeronautics, computer science and electronics. The focus of the thesis has been on the development of a control method to enable the helicopter to follow 3D paths. Additionally, a helicopter simulation tool has been developed in order to test the control system before flight-tests. The thesis also presents an implementation and experimental evaluation of a sensor fusion technique based on a Kalman filter applied to a vision based autonomous landing problem. Extensive experimental flight-test results are presented.

Temporal Action Logic is a well established logical formalism for reasoning about action and change that has long been used as a formal specification language. Its first-order characterization and explicit time representation makes it a suitable target for automated theorem proving and the application of temporal constraint solvers. We introduce a translation from a subset of Temporal Action Logic to constraint logic programs that takes advantage of these characteristics to make the logic applicable, not just as a formal specification language, but in solving practical reasoning problems. Extensions are introduced that enable the generation of action sequences, thus paving the road for interesting applications in deductive planning. The use of qualitative temporal constraints makes it possible to follow a least commitment strategy and construct partially ordered plans. Furthermore, the logical language and logic program translation is extended with the notion of composite actions that can be used to formulate and execute scripted plans with conditional actions, non-deterministic choices, and loops. The resulting planner and reasoner is integrated with a graphical user interface in our autonomous helicopter research system and applied to logistics problems. Solution plans are synthesized together with monitoring constraints that trigger the generation of recovery actions in cases of execution failures.

This paper describes the approach of the RescueRobots Freiburg team, which is a team of students from the University of Freiburg that originates from the former CS Freiburg team (RoboCupSoccer) and the ResQ Freiburg team (RoboCupRescue Simulation). Furthermore we introduce linkMAV, a micro aerial vehicle platform. Our approach covers RFID-based SLAM and exploration, autonomous detection of relevant 3D structures, visual odometry, and autonomous victim identification. Furthermore, we introduce a custom made 3D Laser Range Finder (LRF) and a novel mechanism for the active distribution of RFID tags.

Most of todayâs autonomous problem solving agents perform their task with the help of problem domain specifications that keep their abstractions fixed. Those abstractions are often selected by human users. We think that the approach with fixed-abstraction domain specifications is very inflexible because it does not allow the agent to focus its limited computational resources on what may be most relevant at the moment. We would like to build agents that dynamically find suitable abstractions depending on relevance for their current task and situation. This idea of dynamic abstraction has recently been considered an important research problem within the area of hierarchical reinforcement learning [1].

The h(m) admissible heuristics for (sequential and temporal) regression planning are defined by a parameterized relaxation of the optimal cost function in the regression search space, where the parameter m offers a trade-off between the accuracy and computational cost of the heuristic. Existing methods for computing the h(m) heuristic require time exponential in m, limiting them to small values (m &lt;= 2). The h(m) heuristic can also be viewed as the optimal cost function in a relaxation of the search space: this paper presents relaxed search, a method for computing this function partially by searching in the relaxed space. The relaxed search method, because it compute h(m) only partially, is computationally cheaper and therefore usable for higher values of m. The (complete) h(2) heuristic is combined with partial hm heuristics , for m = 3, ... computed by relaxed search, resulting in a more accurate heuristic. This use of the relaxed search method to improve on the h(2) heuristic is evaluated by comparing two optimal temporal planners: TP4, which does not use it, and HSP*(a), which uses it but is otherwise identical to TP4. The comparison is made on the domains used in the 2004 International Planning Competition, in which both planners participated. Relaxed search is found to be cost effective in some of these domains, but not all. Analysis reveals a characterization of the domains in which relaxed search can be expected to be cost effective, in terms of two measures on the original and relaxed search spaces. In the domains where relaxed search is cost effective, expanding small states is computationally cheaper than expanding large states and small states tend to have small successor states.

A runtime system for implementation of image processing operations is presented. It is designed for working in a flexible and distributed environment related to the software architecture of a newly developed UAV system. The software architecture can be characterized at a coarse scale as a layered system, with a deliberative layer at the top, a reactive layer in the middle, and a processing layer at the bottom. At a finer scale each of the three levels is decomposed into sets of modules which communicate using CORBA, allowing system development and deployment on the UAV to be made in a highly flexible way. Image processing takes place in a dedicated module located in the process layer, and is the main focus of the paper. This module has been designed as a runtime system for data flow graphs, allowing various processing operations to be created online and on demand by the higher levels of the system. The runtime system is implemented in Java, which allows development and deployment to be made on a wide range of hardware/software configurations. Optimizations for particular hardware platforms have been made using Java's native interface.

In Proceedings of the 9th International Conference on Relational Methods in Computer Science and 4th International Workshop on Applications of Kleene Algebra (RelMiCS/AKA), pages 388–401. In series: Lecture Notes in Computer Science #4136. Springer. DOI:10.1007/11828563_26.

In the current paper we first show that the fixpoint theory of equality is decidable. The motivation behind considering this theory is that second-order quantifier elimination techniques based on a theorem given in [16], when successful, often result in such formulas. This opens many applications, including automated theorem. proving, static verification of integrity constraints in databases as well as reasoning with weakest sufficient and strongest necessary conditions.

The present book is a festschrift in honor of Luigia Carlucci Aiello. The 18 articles included are written by former students, friends, and international colleagues, who have cooperated with Luigia Carlucci Aiello, scientifically or in AI boards or committees. The contributions by reputed researchers span a wide range of AI topics and reflect the breadth and depth of Aiello's own work

Per Olof Pettersson and Patrick Doherty.
2006.Probabilistic roadmap based path planning for an autonomous unmanned helicopter.

Journal of Intelligent & Fuzzy Systems, 17(4):395–405. IOS Press.

The emerging area of intelligent unmanned aerial vehicle (UAV) research has shown rapid development in recent years and offers a great number of research challenges for artificial intelligence. For both military and civil applications, there is a desire to develop more sophisticated UAV platforms where the emphasis is placed on development of intelligent capabilities. Imagine a mission scenario where a UAV is supplied with a 3D model of a region containing buildings and road structures and is instructed to fly to an arbitrary number of building structures and collect video streams of each of the building's respective facades. In this article, we describe a fully operational UAV platform which can achieve such missions autonomously. We focus on the path planner integrated with the platform which can generate collision free paths autonomously during such missions. Both probabilistic roadmap-based (PRM) and rapidly exploring random trees-based (RRT) algorithms have been used with the platform. The PRM-based path planner has been tested together with the UAV platform in an urban environment used for UAV experimentation.

Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment and to supply such state information to other nodes in the distributed network in which it is embedded. These structures must be managed and made accessible to deliberative and reactive functionalities whose successful operation is dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environments. DyKnow is a knowledge processing middleware framework which provides a set of functionalities for contextually creating, storing, accessing and processing such structures. The framework is implemented and has been deployed as part of a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used to create more abstract entity and state representations of the world which can then be used for situation awareness by an unmanned aerial vehicle in achieving mission goals. We also show that the framework is a working instantiation of many aspects of the JDL data fusion model.

Patrick Doherty, John Mylopoulos and Christopher Welty.
2006.Proceedings of the 10th International Conference on Principles of Knowledge Representation and Reasoning.

Conference Proceedings.AAAI Press. ISBN:978-1-57735-281-5.

he National Conference on Artificial Intelligence remains the bellwether for research in artificial intelligence. Leading AI researchers and practitioners as well as scientists and engineers in related fields present theoretical, experimental, and empirical results, covering a broad range of topics that include principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. The Innovative Applications of Artificial Intelligence conference highlights successful applications of AI technology; explores issues, methods, and lessons learned in the development and deployment of AI applications; and promotes an interchange of ideas between basic and applied AI. This volume presents the proceedings of the latest conferences, held in July, 2006.

In Proceedings of the International Symposium on Practical Cognitive Agents and Robots (PCAR). UWA Press. ISBN:1-74052-130-7.DOI:10.1145/1232425.1232444.

Temporal Action Logic is a well established logical formalism for reasoning about action and change using an explicit time representation that makes it suitable for applications that involve complex temporal reasoning. We take advantage of constraint satisfaction technology to facilitate such reasoning through temporal constraint networks. Extensions are introduced that make generation of action sequences possible, thus paving the road for interesting applications in deductive planning. The extended formalism is encoded as a logic program that is able to realize a least commitment strategy that generates partial order plans in the context of both qualitative and quantitative temporal constraints.

Description logics refer to a family of formalisms concentrated around concepts, roles and individuals. They belong to the most frequently used knowledge representation formalisms and provide a logical basis to a variety of well known paradigms. The main reasoning tasks considered in the area of description logics are those reducible to subsumption. On the other hand, any knowledge representation system should be equipped with a more advanced reasoning machinery. Therefore in the current paper we make a step towards integrating description logics with second-order reasoning. One of the important motivations behind introducing second-order formalism follows from the fact that many forms of commonsense and nonmonotonic reasoning used in AI can be modelled within the second-order logic. To achieve our goal we first extend description logics with a possibility to quantify over concepts. Since one of the main criticisms against the use of second-order formalisms is their complexity, we next propose second-order quantifier elimination techniques applicable to a large class of description logic formulas. Finally we show applications of the techniques, in particular in reasoning with circumscribed concepts and approximated terminological formulas.

In Proceedings of the Workshop on Knowledge and Reasoning for Language Processing (KRAQ). Association for Computational Linguistics.

We consider a logicist approach to natural language understanding based on the translation of a quasi-logical form into a temporal logic, explicitly constructed for the representation of action and change, and the subsequent reasoning about this semantic structure in the context of a background knowledge theory using automated theorem proving techniques. The approach is substantiated through a proof-of-concept question answering system implementation that uses a head-driven phrase structure grammar developed in the Linguistic Knowledge Builder to construct minimal recursion semantics structures which are translated into a Temporal Action Logic where both the SNARK automated theorem prover and the Allegro Prolog logic programming environment can be used for reasoning through an interchangeable compilation into first-order logic or logic programs respectively.

H.Joe Steinhauer.
2006.Qualitative Reconstruction and Update of an Object Constellation.

In Proceedings of the Spatial and Temporal Reasoning Workshop at the 17th European Conference on Artificial Intelligence (ECAI).

We provide a technique for describing, reconstructing and updating an object constellation of moving objects. The relations between the constituent objects, in particular axis-parallel and diagonal relations, are verbally expressed using the double cross method for qualitatively characterizing relations between pairs of objects. The same underlying representation is used to reconstruct the constellation from the given description.

The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. While the application domains in which they are currently used are still predominantly military in nature, in the future we can expect wide spread usage in thecivil and commercial sectors. In order to insert such vehicles into commercial airspace, it is inherently important that these vehicles can generate collision-free motion plans and also be able to modify such plans during theirexecution in order to deal with contingencies which arise during the course of operation. In this paper, wepresent a fully deployed autonomous unmanned aerial vehicle, based on a Yamaha RMAX helicopter, whichis capable of navigation in urban environments. We describe a motion planning framework which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Treestogether with a path following controller that is used during path execution. Integrating deliberative services, suchas planners, seamlessly with control components in autonomous architectures is currently one of the major open problems in robotics research. We show how the integration between the motion planning framework and thecontrol kernel is done in our system.Additionally, we incorporate a dynamic path reconfigurability scheme. It offers a surprisingly efficient method for dynamic replanning of a motion plan based on unforeseen contingencies which may arise during the execution of a plan. Those contingencies can be inserted via ground operator/UAV interaction to dynamically change UAV flight paths on the fly. The system has been verified through simulation and in actual flight. We present empirical results of the performance of the framework and the path following controller.

In Software Demonstrations at the International Conference on Automated Planning Scheduling (ICAPS-SD), pages 36–37.

The Autonomous UAV Technologies Laboratory at LinkÃ¶ping University, Sweden, has been developing fully autonomous rotor-based UAV systems in the mini- and micro-UAV class. Our current system design is the result of an evolutionary process based on many years of developing, testing and maintaining sophisticated UAV systems. In particular, we have used the Yamaha RMAX helicopter platform(Fig. 1) and developed a number of micro air vehicles from scratch.

In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabilistic Roadmaps and Rapidly Exploring Random Trees. Additionally, we incorporate dynamic reconfigurability into the framework by integrating the motion planners with the control kernel of the UAV in a novel manner with little modification to the original algorithms. The framework has been verified through simulation and in actual flight. Empirical results show that these techniques used with such a framework offer a surprisingly efficient method for dynamically reconfiguring a motion plan based on unforeseen contingencies which may arise during the execution of a plan. The framework is generic and can be used for additional platforms.

In the current paper we provide two methods for quantifier elimination applicable to a large class of formulas of the elementary set theory. The first one adapts the Ackermann method [1] and the second one adapts the fixpoint method of [20]. We show applications of the proposed techniques in the theory of correspondence between modal logics and elementary set theory. The proposed techniques can also be applied in an automated generation of proof rules based on the semantic-based translation of axioms of a given logic into the elementary set theory.

The basis for the material in this book centers around a long term research project with autonomous unmanned aerial vehicle systems. One of the main research topics in the project is knowledge representation and reasoning. The focus of the research has been on the development of tractable combinations of approximate and nonmonotonic reasoning systems. The techniques developed are based on intuitions from rough set theory. Efforts have been made to take theory into practice by instantiating research results in the context of traditional relational database or deductive database systems. This book contains a cohesive, self-contained collection of many of the theoretical and applied research results that have been achieved in this project and for the most part pertain to nonmonotonic and approximate reasoning systems developed for an experimental unmanned aerial vehicle system used in the project. This book should be of interest to the theoretician and applied researcher alike and to autonomous system developers and software agent and intelligent system developers.

This paper describes an experimental platform for approximate knowledge databases called the Approximate Knowledge Database (AKDB), based on a semantics inspired by rough sets. The implementation is based upon the use of a standard SQL database to store logical facts, augmented with several query interface layers implemented in JAVA through which extensional, intensional and local closed world nonmonotonic queries in the form of crisp or approximate logical formulas can be evaluated tractably. A graphical database design user interface is also provided which simplifies the design of databases, the entering of data and the construction of queries. The theory and semantics for AKDBs is presented in addition to application examples and details concerning the database implementation.

As long as there have been computers, one goal has been to be able to communicate with them using natural language. It has turned out to be very hard to implement a dialog system that performs as well as a human being in an unrestricted domain, hence most dialog systems today work in small, restricted domains where the permitted dialog is fully controlled by the system.In this thesis we present two dialog systems for communicating with an autonomous agent:The first system, the WITAS RDE, focuses on constructing a simple and failsafe dialog system including a graphical user interface with multimodality features, a dialog manager, a simulator, and development infrastructures that provides the services that are needed for the development, demonstration, and validation of the dialog system. The system has been tested during an actual flight connected to an unmanned aerial vehicle.The second system, CEDERIC, is a successor of the dialog manager in the WITAS RDE. It is equipped with a built-in machine learning algorithm to be able to learn new phrases and dialogs over time using past experiences, hence the dialog is not necessarily fully controlled by the system. It also includes a discourse model to be able to keep track of the dialog history and topics, to resolve references and maintain subdialogs. CEDERIC has been evaluated through simulation tests and user tests with good results.

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called âclassicalâ AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time â makespan â of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the h<sup>m</sup>, additive h<sup>m</sup> and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time, aimed at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods. Results indicate that some plausible such characteristics have been found, but are not entirely conclusive.

Many of the applications that have been proposed for future small unmanned aerial vehicles (UAVs) are at low altitude in areas with many obstacles. A vital component for successful navigation in such environments is a path planner that can find collision free paths for the UAV.Two popular path planning algorithms are the probabilistic roadmap algorithm (PRM) and the rapidly-exploring random tree algorithm (RRT).Adaptations of these algorithms to an unmanned autonomous helicopter are presented in this thesis, together with a number of extensions for handling constraints at different stages of the planning process.The result of this work is twofold:First, the described planners and extensions have been implemented and integrated into the software architecture of a UAV. A number of flight tests with these algorithms have been performed on a physical helicopter and the results from some of them are presented in this thesis.Second, an empirical study has been conducted, comparing the performance of the different algorithms and extensions in this planning domain. It is shown that with the environment known in advance, the PRM algorithm generally performs better than the RRT algorithm due to its precompiled roadmaps, but that the latter is also usable as long as the environment is not too complex. The study also shows that simple geometric constraints can be added in the runtime phase of the PRM algorithm, without a big impact on performance. It is also shown that postponing the motion constraints to the runtime phase can improve the performance of the planner in some cases.

In this paper, we propose a new belief revision operator, together with a method of its calculation. Our formalization differs from most of the traditional approaches in two respects. Firstly, we formally distinguish between defeasible observations and indefeasible knowledge about the considered world. In particular, our operator is differently specified depending on whether an input formula is an observation or a piece of knowledge. Secondly, we assume that a new observation, but not a new piece of knowledge, describes exactly what a reasoning agent knows at the moment about the aspect of the world the observation concerns.

The relation of similarity is essential in understanding and developing frameworks for reasoning with vague and approximate concepts. There is a wide spectrum of choice as to what properties we associate with similarity and such choices determine the nature of vague and approximate concepts defined in terms of these relations. Additionally, robotic systems naturally have to deal with vague and approximate concepts due to the limitations in reasoning and sensor capabilities. Halpern [1] introduces the use of subjective and objective states in a modal logic formalizing vagueness and distinctions in transitivity when an agent reasons in the context of sensory and other limitations. He also relates these ideas to a solution to the Sorities and other paradoxes. In this paper, we generalize and apply the idea of similarity and tolerance spaces [2,3,4,5], a means of constructing approximate and vague concepts from such spaces and an explicit way to distinguish between an agentâs objective and subjective states. We also show how some of the intuitions from Halpern can be used with similarity spaces to formalize the above-mentioned Sorities and other paradoxes.

PhD Thesis.In series: Linköping Studies in Science and Technology. Dissertations #946. Linköping University Electronic Press. 139 pages. ISBN:91-85297-98-4.Note: This work has been supported by University of Kalmar and the Knowledge Foundation.

The number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to design user-driven information extraction systems where non-expert users are able to adapt them to new domains and tasks. It is difficult to design general extraction systems that do not require expert skills or a large amount of work from the user. Therefore, it is difficult to increase the number of domains and tasks. A possible alternative is to design user-driven systems, which solve that problem by letting a large number of non-expert users adapt the systems themselves. To accomplish this goal, the systems need to become more intelligent and able to learn to extract with as little given information as possible.The type of information extraction system that is in focus for this thesis is semi-structured information extraction. The term semi-structured refers to documents that not only contain natural language text but also additional structural information. The typical application is information extraction from World Wide Web hypertext documents. By making effective use of not only the link structure but also the structural information within each such document, user-driven extraction systems with high performance can be built.There are two different approaches presented in this thesis to solve the user-driven extraction problem. The first takes a machine learning approach and tries to solve the problem using a modified $Q(\lambda)$ reinforcement learning algorithm. A problem with the first approach was that it was difficult to handle extraction from the hidden Web. Since the hidden Web is about 500 times larger than the visible Web, it would be very useful to be able to extract information from that part of the Web as well. The second approach is called the hidden observation approach and tries to also solve the problem of extracting from the hidden Web. The goal is to have a user-driven information extraction system that is also able to handle the hidden Web. The second approach uses a large part of the system developed for the first approach, but the additional information that is silently obtained from the user presents other problems and possibilities.An agent-oriented system was designed to evaluate the approaches presented in this thesis. A set of experiments was conducted and the results indicate that a user-driven information extraction system is possible and no longer just a concept. However, additional work and research is necessary before a fully-fledged user-driven system can be designed.

A general similarity-based algorithm for extracting ontologies from data has been provided in [1]. The algorithm works over arbitrary approximation spaces, modeling notions of similarity and mereological part-of relations (see, e.g., [2, 3, 4, 5]). In the current paper we propose a novel technique of machine learning similarity on tuples on the basis of similarities on attribute domains. The technique reflects intuitions behind tolerance spaces of [6] and similarity spaces of [7]. We illustrate the use of the technique in extracting ontologies from data.

Knowledge representation technologies play a fundamental role in any autonomous system that includes deliberative capability and that internalizes models of its internal and external environments. Integrating both high- and low-end autonomous functionality seamlessly in autonomous architectures is currently one of the major open problems in robotics research. UAVs offer especially difficult challenges in comparison with ground robotic systems due to the often tight time constraints and safety considerations that must be taken into account. This article provides an overview of some of the knowledge representation technologies and deliberative capabilities developed for a fully deployed autonomous unmanned aerial vehicle system to meet some of these challenges.

The overall objective of the Wallenberg Laboratory for Information Technology and Autonomous Systems (WITAS) at Linkoping University is the development of an intelligent command and control system, containing active-vision sensors, which supports the operation of an unmanned air vehicle (UAV). One of the UA V platforms of choice is the R5O unmanned helicopter, by Yamaha.The present version of the UAV platform is augmented with a camera system. This is enough for performing missions like site mapping, terrain exploration, in which the helicopter motion can be rather slow. But in tracking missions, and obstacle avoidance scenarios, involving high-speed helicopter motion, robust performance for the visual-servoing scheme is desired. Robustness in this case is twofold: 1) w.r.t time delays introduced by the image processing; and 2) w.r.t disturbances, parameter uncertainties and unmodeled dynamics which reflect on the feature position in the image, and the camera pose.With this goal in mind, we propose to explore the possibilities for the design of fuzzy controllers, achieving stability, robust and minimal-cost performance w.r.t time delays and unstructured uncertainties for image feature tracking, and test a control solution in both simulation and on real camera platforms. Common to both are model-based design by the use of nonlinear control approaches. The performance of these controllers is tested in simulation using the nonlinear geometric model of a pin-hole camera. Then we implement and test the reSUlting controller on the camera platform mounted on the UAV.

In Proceedings of the 20th national ´Conference on Artificial Intelligence (AAAI). AAAI Press. ISBN:1-57735-236-X.

Admissible heuristics are critical for effective domain-independent planning when optimal solutions must be guaranteed. Two useful heuristics are the <em>h<sup>m</sup></em> heuristics, which generalize the reachability heuristic underlying the planning graph, and pattern database heuristics. These heuristics, however, have serious limitations: reachability heuristics capture only the cost of critical paths in a relaxed problem, ignoring the cost of other relevant paths, while PDB heuristics, additive or not, cannot accommodate too many variables in patterns, and methods for automatically selecting patterns that produce good estimates are not known.We introduce two refinements of these heuristics: First, the additive <em>h<sup>m</sup></em> heuristic which yields an admissible sum of <em>h<sup>m</sup></em> heuristics using a partitioning of the set of actions. Second, the constrained PDB heuristic which uses constraints from the original problem to strengthen the lower bounds obtained from abstractions.The new heuristics depend on the way the actions or problem variables are partitioned. We advance methods for automatically deriving additive <em>h<sup>m</sup></em> and PDB heuristics from STRIPS encodings. Evaluation shows improvement over existing heuristics in several domains, although, not surprisingly, no heuristic dominates all the others over all domains.

The emerging area of intelligent unmanned aerialvehicle (UAV) research has shown rapid development in recentyears and offers a great number of research challenges for artificialintelligence. For both military and civil applications, thereis a desire to develop more sophisticated UAV platforms wherethe emphasis is placed on development of intelligent capabilities.Imagine a mission scenario where a UAV is supplied with a 3Dmodel of a region containing buildings and road structures andis instructed to fly to an arbitrary number of building structuresand collect video streams of each of the buildingâs respectivefacades. In this article, we describe a fully operational UAVplatform which can achieve such missions autonomously. Wefocus on the path planner integrated with the platform which cangenerate collision free paths autonomously during such missions.It is based on the use of probabilistic roadmaps. The path plannerhas been tested together with the UAV platform in an urbanenvironment used for UAV experimentation.

We present a robotic dialogue system built on casebased reasoning. The system is capable of solving references and manage sub-dialogues in a dialogue with an operator in natural language. The approach to handle dialogue acts and physical acts in a unison manner together with the use of plans and subplans makes the system very flexible. This flexibility is used for learning purposes where the operator teaches the system a new word and the new knowledge can directly be integrated and used in the old plans. The learning from explanation capability makes the system adaptable to the operator's use of language and the domain it is currently operating in. The implementation of a case-based planner suggested in the paper will further increase the learning and adaptation degree.

In Proceedings of the 9th workshop on the semantics and pragmatics of dialogue (SemDial).

We present a discourse model integrated with a case-based reasoning dialogue system which learns from experience. The discourse model is capable of solving references, manage subdialogues and respect the current topic in a dialogue in natural language. The framework is flexible enough not to disturb the learning functions, but allows dynamic changes to a large extent. The system is tested in a traffic surveillance domain together with a simulated UAV and is found to be robust and reliable.

Though the exact definition of the boundary between intelligent and non-intelligent artifacts has been a subject of much debate, one aspect of intelligence that many would deem essential is <em>deliberation</em>: Rather than reacting \"instinctively\" to its environment, an intelligent system should also be capable of <em>reasoning</em> about it, reasoning about the effects of actions performed by itself and others, and creating and executing plans, that is, determining which actions to perform in order to achieve certain goals. True deliberation is a complex topic, requiring support from several different sub-fields of artificial intelligence. The work presented in this thesis spans two of these partially overlapping fields, beginning with <em>reasoning about action and change</em> and eventually moving over towards <em>planning</em>.The qualification problem relates to the difficulties inherent in providing, for each action available to an agent, an exhaustive list of all qualifications to the action, that is, all the conditions that may prevent the action from being executed in the intended manner. The first contribution of this thesis is a framework for <em>modeling qualifications</em> in Temporal Action Logic (TAL).As research on reasoning about action and change proceeds, increasingly complex and interconnected domains are modeled in increasingly greater detail. Unless the resulting models are structured consistently and coherently, they will be prohibitively difficult to maintain. The second contribution is a framework for <em>structuring TAL domains</em> using object-oriented concepts.Finally, the second half of the thesis is dedicated to the task of <em>planning</em>. TLplan pioneered the idea of using domain-specific control knowledge in a temporal logic to constrain the search space of a forward-chaining planner. We develop a new planner called TALplanner, based on the same idea but with fundamental differences in the way the planner verifies that a plan satisfies control formulas. T ALplanner generates concurrent plans and can take resource constraints into account. The planner also applies several new automated domain analysis techniques to control formulas, further increasing performance by orders of magnitude for many problem domains.

In this paper we describe a qualitative approach for natural language communication about vehicle traffic. It is an intuitive and simple model that can be used as the basis for defining more detailed position descriptions and transitions. It can also function as a framework for relating different aggregation levels. We apply a diagrammatic abstraction of traffic that mirrors the different possible interpretations of it and with this the different mental abstractions that humans might make. The abstractions are kept in parallel and according to the communicative context it will be switched to the corresponding interpretation.

In this paper, I explore the idea that there are âpatternsâ,analogous to software design patterns, in the kind of task proceduresthat frequently form the reactive component of architectures for intelligentautonomous systems. The investigation is carried out mainlywithin the context of the WITAS UAV project.

Biochemical pathways or networks are generic representations used to model many different types of complex functional and physical interactions in biological systems. Models based on experimental results are often incomplete, e.g., reactions may be missing and only some products are observed. In such cases, one would like to reason about incomplete network representations and propose candidate hypotheses, which when represented as additional reactions, substrates, products, would complete the network and provide causal explanations for the existing observations. In this paper, we provide a logical model of biochemical pathways and show how abductive hypothesis generation may be used to provide additional information about incomplete pathways. Hypothesis generation is achieved using weakest and strongest necessary conditions which represent these incomplete biochemical pathways and explain observations about the functional and physical interactions being modeled. The techniques are demonstrated using metabolism and molecular synthesis examples.

Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agent's embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.

Although many formalisms for reasoning about action and change have been proposed in the literature, any concrete examples provided in such articles have primarily consisted of tiny domains that highlight some particular aspect or problem. However, since some of the classical problems are now completely or partially solved and since powerful tools are becoming available, it is now necessary to start modeling more complex domains. This article presents a methodology for handling such domains in a systematic manner using an object-oriented framework and provides several examples of the elaboration tolerance exhibited by the resulting models. (C) 2003 Elsevier B.V. All rights reserved.

Abstract-This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties in a visual-servoing control scheme. The Takagi-Sugeno (T-S) fuzzy model is adopted for the nonlinear geometric model of a pin-hole camera, which presents second-order nonlinearities. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a multi-objective robust fuzzy controller design is proposed for the uncertain fuzzy system. The sufficient conditions are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated through numerical simulation, then tested on a EVI-D31 SONY camera.

In this paper we address the design of a fuzzy flight controller that achieves stable and robust -aggressive- manoeuvrability for an unmanned helicopter. The fuzzy flight controller proposed consists of a combination of a fuzzy gain scheduler and linguistic (Mamdani-type) controller. The fuzzy gain scheduler is used for stable and robust altitude, roll, pitch, and yaw control. The linguistic controller is used to compute the inputs to the fuzzy gain scheduler, i.e., desired values for roll, pitch, and yaw at given desired altitude and horizontal velocities. The flight controller is obtained and tested in simulation using a realistic nonlinear MIMO model of a real unmanned helicopter platform, the APID-MK

In IEEE International Conference on Fuzzy Systems Fuzz-IEEE 2004,2004.

This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a robust controller design method is proposed. The sufficient conditions and the design procedure are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated of a visual-servoing control problem

In Proceedings of the 9th International Conference on the Principles of Knowledge Representation and Reasoning, pages 731–732. AAAI Press. ISBN:978-1-57735-199-3.

The emerging area of intelligent unmanned aerial vehicle (UAV) research has shown rapid development in recent years and offers a great number of research challenges for artificial intelligence and knowledge representation. For both military and civilian applications, there is a desire to develop more sophisticated UAV platforms where the emphasis is placed on intelligent capabilities and their integration in complex distributed software architectures. Such architectures should support the integration of deliberative, reactive and control functionalities in addition to the UAVâs integration with larger network centric systems. In my talk I will present some of the research and results from a long term basic research project with UAVs currently being pursued at LinkÃ¶ping University, Sweden. The talk will focus on knowledge representation techniques used in the project and the support for these techniques provided by the software architecture developed for our UAV platform, a Yamaha RMAX helicopter. Additional focus will be placed on some of the planning and execution monitoring functionality developed for our applications in the areas of traffic monitoring, surveying and photogrammetry and emergency services assistance.

Any autonomous system embedded in a dynamic and changing environment must be able to create qualitative knowledge and object structures representing aspects of its environment on the fly from raw or preprocessed sensor data in order to reason qualitatively about the environment. These structures must be managed and made accessible to deliberative and reactive functionalities which are dependent on being situationally aware of the changes in both the robotic agentâs embedding and internal environment. DyKnow is a software framework which provides a set of functionalities for contextually accessing, storing, creating and processing such structures. The system is implemented and has been deployed in a deliberative/reactive architecture for an autonomous unmanned aerial vehicle. The architecture itself is distributed and uses real-time CORBA as a communications infrastructure. We describe the system and show how it can be used in execution monitoring and chronicle recognition scenarios for UAV applications.

A hybrid control system for dynamic path following for an autonomous helicopter is described. The hierarchically structured system combines continuous control law execution with event-driven state machines. Trajectories are defined by a sequence of 3D path segments and velocity profiles, where each path segment is described as a parametric curve. The method can be used in combination with a path planner for flying collision-free in a known environment. Experimental flight test results are shown.

We provide a logical model of biochemical reactions and show how hypothesis generation using weakest sufficient and strongest necessary conditions may be used to provide additional information in the context of an incomplete model of metabolic pathways.

In Proceedings of the 7th International Conference on Information Fusion, pages 175–182. ISIF. ISBN:91-7056-115-X.

In this paper, we propose a framework that provides software and robotic agents with the ability to ask approximate questions to each other in the context of heterogeneous and contextually limited perceptual capabilities. The framework focuses on situations where agents have varying ability to perceive their environments. These limitations on perceptual capability are formalized using the idea of tolerance spaces. It is assumed that each agent has one or more approximate databases where approximate relations are represented using intuitions from rough set theory. It is shown how sensory and other limitations can be taken into account when constructing approximate databases for each respective agent. Complex relations inherit the approximativeness inherent in the sensors and primitive relations used in their definitions. Agents then query these databases and receive answers through the filters of their perceptual limitations as represented by tolerance spaces and approximate queries. The techniques used are all tractable.

This paper focuses on the use and interpretation of approximate databases where both rough sets and indiscernibility partitions are generalized and replaced by approximate relations and similarity spaces. Similarity spaces are used to define neighborhoods around individuals and these in turn are used to define approximate sets and relations. There is a wide spectrum of choice as to what properties the similarity relation should have and how this affects the properties of approximate relations in the database. In order to make this interaction precise, we propose a technique which permits specification of both approximation and similarity constraints on approximate databases and automatic translation between them. This technique provides great insight into the relation between similarity and approximation and is similar to that used in modal correspondence theory. In order to automate the translations, quantifier elimination techniques are used.

In this paper, we propose a framework that provides software and robotic agents with the ability to ask approximate questions to each other in the context of heterogeneous ontologies and heterogeneous perceptive capabilities.The framework combines the use of logic-based techniques with ideas from approximate reasoning. Initial queries by an agent are transformed into approximate queries using weakest sufficient and strongest necessary conditions on the query and are interpreted as lower and upper approximations on the query. Once the base communication ability is provided, the framework is extended to situations where there is not only a mismatch between agent ontologies, but the agents have varying ability to perceive their environments. This will affect each agentâs ability to ask and interpret results of queries. Limitations on perceptive capability are formalized using the idea of tolerance spaces.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms.It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

Soft computing comprises various paradigms dedicated to approximately solving real-world problems, e.g., in decision making, classification or learning; among these paradigms are fuzzy sets, rough sets, neural networks, and genetic algorithms.It is well understood now in the soft computing community that hybrid approaches combining various paradigms provide very promising attempts to solving complex problems. Exploiting the potential and strength of both neural networks and rough sets, this book is devoted to rough-neurocomputing which is also related to the novel aspect of computing based on information granulation, in particular to computing with words. It provides foundational and methodological issues as well as applications in various fields.

We investigate two methods of using limited search to improve admissible heuristics for planning, similar to pattern databases and pattern searches. We also develop a new algorithm for searching AND/OR graphs

This paper states the need for interactive teaching materials for programming languages within the area of modeling and simulation. We propose an interactive teaching material for the modeling language Modelica inspired by existing tutoring systems for Java and Scheme. The purpose of this new teaching material, called DrModelica, is to facilitate the learning of Modelica through an environment that integrates programming, program documentation and visualization. The teaching material is intended to be used for modeling and simulation related courses at the undergraduate and graduate level.

In Proceedings of the 44th Conference on Simulation and Modeling (SIMS). Malardalen University. ISBN:91-631-4716-5.

This paper states the need for interactive teaching materials for programming languages within the area of modeling and simulation. We propose an interactive teaching material for the modeling language Modelica inspired by existing tutoring systems for Java and Scheme.The purpose of this new teaching material, called DrModelica, is to facilitate the learning of Modelica in a modeling and simulation environment. We have developed two versions of DrModelica, one that is based on Mathematica and another that is intended for the web. With the web version of DrModelica we hope for an increased usage of Modelica.

In Proceedings of the 3rd International Modelica Conference. Modelica Association.

This paper states the need for interactive teaching materials for programming languages within the area of modeling and simulation. We propose an interactive teaching material for the modeling language Modelica inspired by existing tutoring systems for Java and Scheme.The purpose of this new teaching material, called DrModelica, is to facilitate the learning of Modelica through an environment that integrates programming, program documentation and visualization. The teaching material is intended to be used for modeling and simulation related courses at the undergraduate and graduate level.

The WITAS project addresses the design of an intelligent, autonomous UAV (Unmanned Aerial Vehicle), in our case a helicopter. Its dialogue-system subprojects address the design of a deliberative system for natural-language and graphical dialogue with that robotic UAV. This raises new issues both for dialogue and for reasoning in real time. The following topics have been particularly important for us in various stages of the work in these subprojects: - spatiotemporal reference in the dialogue, including reference to past events and to planned or expected, future events - mixed initiative in the dialogue architecture of a complex system consisting of both dialogue-related components (speech, grammar, etc) and others (simulation, event recognition, interface to robot) and more recently as well - identification of a dialogue manager that is no more complex than what is required by the application - uniform treatment of different types of events, including the robot's own actions, observed events, communication events, and dialogue-oriented deliberation events - a logic of time, action, and spatiotemporal phenomena that facilitates the above. This paper gives a brief overview of the WITAS project as a whole, and then addresses the approaches that have been used and that are presently being considered in the work on two generations of dialogue subsystems.

In the paper we present a purely logical approach to estimating computational complexity of potentially intractable problems. The approach is based on descriptive complexity and second-order quantifier elimination techniques. We illustrate the approach on the case of the transversal hypergraph problem, TRANSHYP, which has attracted a great deal of attention. The complexity of the problem remains unsolved for over twenty years. Given two hypergraphs, G and H, TRANSHYP depends on checking whether G = H-d, where H-d is the transversal hypergraph of H. In the paper we provide a logical characterization of minimal transversals of a given hypergraph and prove that checking whether G subset of or equal to H-d is tractable. For the opposite inclusion the Problem still remains open. However, we interpret the resulting quantifier sequences in terms of determinism and bounded nondeterminism. The results give better upper bounds than those known from the literature, e.g., in the case when hypergraph H, has a sub-logarithmic number of hyperedges and (for the deterministic case) all hyperedges have the cardinality bounded by a function sub-linear wrt maximum of sizes of G and H.

TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal logic formulas in order to prune irrelevant parts of the search space. TALplanner recently participated in the third International Planning Competition, which had a clear emphasis on increasing the complexity of the problem domains being used as benchmark tests and the expressivity required to represent these domains in a planning system. Like many other planners, TALplanner had support for some but not all aspects of this increase in expressivity, and a number of changes to the planner were required. After a short introduction to TALplanner, this article describes some of the changes that were made before and during the competition. We also describe the process of introducing suitable domain knowledge for several of the competition domains.

Due to its efficiency, defeasible logic is one of the most interesting non-monotonic formalisms. Unfortunately, the logic has one major limitation: it does not properly deal with cyclic defeasible rules. In this paper, we provide a new variant of defeasible logic, using CAKE method. The resulting formalism is tractable and properly deals with circular defeasible rules.

Currently, there is a great deal of interest in developing tools for the generation and use of ontologies on the WWW. These knowledge structures are considered essential to the success of the semantic web, the next phase in the evolution of the WWW. Much recent work with ontologies assumes that the concepts used as building blocks are crisp as opposed to approximate. It is a premise of this paper that approximate concepts and ontologies will become increasingly more important as the semantic web becomes a reality. We propose a framework for specifying, generating and using approximate ontologies. More specifically, (1) a formal framework for defining approximate concepts, ontologies and operations on approximate concepts and ontologies is presented. The framework is based on intuitions from rough set theory, (2) algorithms for automatically generating approximate ontologies from traditional crisp ontologies or from large data sets together with additional knowledge are presented. The knowledge will generally be related to similarity measurements between individual objects in the data sets, or constraints of a logical nature which rule out particular constellations of concepts and dependencies in generated ontologies. The techniques for generating approximate ontologies are parameterizable. The paper provides specific instantiations and examples.

While face animation is still considered one of the toughesttasks in computer animation, its potential application range israpidly moving from the classical field of film production intogames, communications, news delivery and commerce. Tosupport such novel applications, it is important to enableproduction and delivery of face animation on a wide range ofplatforms, from high-end animation systems to the web, gameconsoles and mobile phones. Our goal is to offer a frameworkof tools interconnected by standard formats and protocols andcapable of supporting any imaginable application involvingface animation with the desired level of animation quality,automatic production wherever it is possible, and delivery ona wide range of platforms. While this is clearly an ongoingtask, we present the current state of development along withseveral case studies showing that a wide range of applicationsis already enabled.

When invoking a function or a procedure in an ordinary programming language, it is normally assumed that the arguments may be given as composite expressions, and that they are not restricted to atomic constants or variable symbols. However, although active web pages in HTML-based web servers can be viewed as a kind of procedures, they do not enjoy the same flexibility. The present paper reports on a software package that extends the embedded web server in the ACL (Allegro Common Lisp) system and that provides it with the kind of functional flavor just described. In passing, the software also adds a number of other convenience measures to the LHTML (Lisp-encoded HTML) of the ACL server.

The Software Individuals Architecture (SIA) is a framework fordefining software systems that are capable of self-modification and of reproductionon the level of an interpretive programming language. In abstractterms, a self-modifying system is a labelled tree containing scripts at someof its nodes; these scripts are effectively programs. A computation in sucha system executes a specific script. In doing so it maintains a local computationalstate, but it also uses and updates the labelled tree. The labelledtree, the local computational state, and the command language used for thescripts are all designed in such a way as to support self-modification andreproduction in a structured and orderly fashion.We have defined a practical system of this kind both on an abstract andformal level and as an implementation using Lisp as the host language. Thisarchitecture has been used as a platform for several applications, including inparticular the speech and natural-language dialogue system for an intelligentautonomous unmanned aerial vehicle (UAV) in the WITAS project. Thearchitecture design has been revised repeatedly as a result of using it for thisapplication as well as several others.

In Proceedings of the 26th German Conference on Artificial Intelligence (KI), pages 475–489. In series: Lecture Notes in Computer Science #2821. Springer. DOI:10.1007/978-3-540-39451-8_35.

In traditional approaches to knowledge representation, notions such as tolerance measures on data, distance between objects or individuals, and similarity measures between primitive and complex data structures are rarely considered. There is often a need to use tolerance and similarity measures in processes of data and knowledge abstraction because many complex systems which have knowledge representation components such as robots or software agents receive and process data which is incomplete, noisy, approximative and uncertain. This paper presents a framework for recursively constructing arbitrarily complex knowledge structures which may be compared for similarity, distance and approximativeness. It integrates nicely with more traditional knowledge representation techniques and attempts to bridge a gap between approximate and crisp knowledge representation. It can be viewed in part as a generalization of approximate reasoning techniques used in rough set theory. The strategy that will be used is to define tolerance and distance measures on the value sets associated with attributes or primitive data domains associated with particular applications. These tolerance and distance measures will be induced through the different levels of data and knowledge abstraction in complex representational structures. Once the tolerance and similarity measures are in place, an important structuring generalization can be made where the idea of a tolerance space is introduced. Use of these ideas is exemplified using two application domains related to sensor modeling and communication between agents.

The premise of this paper is that the acquisition, aggregation, merging and use of information requires some new ideas, tools and techniques which can simplify the construction, analysis and use of what we call ephemeral knowledge structures. Ephemeral knowledge structures are used and constructed by granular agents. Each agent contains its own granular information structure and granular information structures of agents can be combined together. The main concept considered in this paper is an information granule. An information granule is a concise conceptual unit that can be integrated into a larger information infrastructure consisting of other information granules and dependencies between them. The novelty of this paper is that it provides a concise and formal definition of a particular view of information granule and its associated operators, as required in advanced knowledge representation applications.

Planning systems rely on knowledge about the problems they have to solve: The problem description and in many cases advice on how to find a solution. This paper is concerned with a third kind of knowledge which we term domain knowledge: Information about the problem that is produced by one component of the planner and used for advice by another. We first distinguish domain knowledge from the problem description and from advice, and argue for the advantages of the explict use of domain knowledge. Then we identify three classes of domain knowledge for which these advantages are most apparent and define a language, DKEL, to represent these classes. DKEL is designed as an extension to PDDL.

The number of domains and tasks where information extraction tools can be used needs to be increased. One way to reach this goal is to construct user-driven information extraction systems where novice users are able to adapt them to new domains and tasks. To accomplish this goal, the systems need to become more intelligent and able to learn to extract information without need of expert skills or time-consuming work from the user.The type of information extraction system that is in focus for this thesis is semistructural information extraction. The term semi-structural refers to documents that not only contain natural language text but also additional structural information. The typical application is information extraction from World Wide Web hypertext documents. By making effective use of not only the link structure but also the structural information within each such document, user-driven extraction systems with high performance can be built.The extraction process contains several steps where different types of techniques are used. Examples of such types of techniques are those that take advantage of structural, pure syntactic, linguistic, and semantic information. The first step that is in focus for this thesis is the navigation step that takes advantage of the structural information. It is only one part of a complete extraction system, but it is an important part. The use of reinforcement learning algorithms for the navigation step can make the adaptation of the system to new tasks and domains more user-driven. The advantage of using reinforcement learning techniques is that the extraction agent can efficiently learn from its own experience without need for intensive user interactions.An agent-oriented system was designed to evaluate the approach suggested in this thesis. Initial experiments showed that the training of the navigation step and the approach of the system was promising. However, additional components need to be included in the system before it becomes a fully-fledged user-driven system.

A number of current planners make use of automatic domain analysis techniques to extract information such as state invariants or necessary goal orderings from a planning domain. There are also planners that allow the user to explicitly specify additional information intended to improve performance. One such planner is TALplanner, which allows the use of domain-dependent temporal control formulas for pruning a forward-chaining search tree. This leads to the question of how these two approaches can be combined. In this paper we show how to make use of automatically generated state invariants to improve the performance of testing control formulas. We also develop a new technique for analyzing control rules relative to control formulas and show how this often allows the planner to automatically strengthen the preconditions of the operators, thereby reducing time complexity and improving the performance of TALplanner by a factor of up to 400 for the largest problems from the AIPS-2000 competition.

In Advances in Plan-Based Control of Robotic Agents: Revised Papers from the International Seminar at Dagstuhl Castle, pages 226–248. In series: Lecture Notes in Computer Science #2466. Springer. DOI:10.1007/3-540-37724-7_14.

This paper addresses the two-way relation between the architecture for cognitive robots on one hand, and a logic of action and change that is adapted to the needs of such robots on the other hand. The relation goes both ways: the logic is used within the architecture, but we also propose that an abstract model of the cognitive robot architecture shall be used for defining the semantics of the logic. For this purpose, we describe a novel architecture called the Double Helix Architecture which, unlike earlier proposals, emphasizes a precise account of the metric discrete timeline and the computational processes that take place along that timeline. The computational model of the Double Helix Architecture corresponds to the semantics of the logic being used, namely the author's Cognitive Robotics Logic which is based on the 'Features and Fluents' theory.

Second-order quantifier elimination in the context of classical logic emerged as a powerful technique in many applications, including the correspondence theory, relational databases, deductive and knowledge databases, knowledge representation, commonsense reasoning and approximate reasoning. In the current paper we generalize the result of [19] by allowing modal operators. This allows us to provide a unifying framework for many applications, that require the use of intensional concepts. Examples of applications of the technique in AI are also provided.

This last volume of the Handbook of Defeasible Reasoning and Uncertainty Management Systems is - together with Volume 6 - devoted to the topics Reasoning and Dynamics, covering both the topics of \"Dynamics of Reasoning,\" where reasoning is viewed as a process, and \"Reasoning about Dynamics,\" which must be understood as pertaining to how both designers of, and agents within dynamic systems may reason about these systems. The present volume presents work done in this context and is more focused on \"reasoning about dynamics,\" viz. how (human and artificial) agents reason about (systems in) dynamic environments in order to control them. In particular modelling frameworks and generic agent models for modelling these dynamic systems and formal approaches to these systems such as logics for agents and formal means to reason about agent-based and compositional systems, and action &amp; change more in general are considered.

In International Conference on Intelligent Robots and Systems (IROS), Workshop on Aerial Robotics: Lausanne, Switzerland.

This paper presents and overview of the basic and applied research carried out by the Computer Vision Laboratory, LinkÃ¶ping University, in the WITAS UAV Project. This work includes customizing and redesigning vision methods to fit the particular needs and restrictions imposed by the UAV platform, e.g., for low-level vision, motion estimation, navigation, and tracking. It also includes a new learning structure for association of perception-action activations, and a runtime system for implementation and execution of vision algorithms. The paper contains also a brief introduction to the WITAS UAV Project.

Introduction: Logic engineering often involves the development of modeling tools and inference mechanisms (both standard and non-standard) which are targeted for use in practical applications where expressiveness in representation must be traded off for efficiency in use. Some representative examples of such applications would be the structuring and querying of knowledge on the semantic web, or the representation and querying of epistemic states used with softbots, robots or smart devices. In these application areas, declarative representations of knowledge enhance the functionality of such systems and also provide a basis for insuring the pragmatic properties of modularity and incremental composition. In addition, the mechanisms developed should be tractable, but at the same time, expressive enough to represent such aspects as default reasoning, or approximate or incomplete representations of the environments in which the entities in question are embedded or used, be they virtual or actual. [...]

Recently substantial research has been devoted to Unmanned Aerial Vehicles (UAVs). One of a UAV's most demanding subsystem is vision. The vision subsystem must dynamically combine different algorithms as the UAVs goal and surrounding change. To fully utilize the available hardware, a run time system must be able to vary the quality and the size of regions the algorithms are applied to, as the number of image processing tasks changes. To allow this the run time system and the underlying computational model must be integrated. In this paper we present a computational model suitable for integration with a run time system. The computational model is called Image Processing Data Flow Graph (IP-DFG). IP-DFG has been developed for modeling of complex image processing algorithms. IP-DFG is based on data flow graphs, but has been extended with hierarchy and new rules for token consumption, which makes the computational model more flexible and more suitable for human interaction. In this paper we also show that IP-DFGs are suitable for modelling expressions, including data dependent decisions and iterations, which are common in complex image processing algorithms.

The WITAS project aims to develop technologies to enable an Unmanned Airial Vehicle (UAV) to operate autonomously and intelligently, in applications such as traffic surveillance and remote photogrammetry. Many of the necessary control and reasoning tasks, e.g. state estimation, reidentification, planning and diagnosis, involve prediction as an important component. Prediction relies on models, and such models can take a variety of forms. Model design involves many choices with many alternatives for each choice, and each alternative carries advantages and disadvantages that may be far from obvious. In spite of this, and of the important role of prediction in so many areas, the problem of predictive model design is rarely studied on its own.In this thesis, we examine a range of applications involving prediction and try to extract a set of choices and alternatives for model design. As a case study, we then develop, evaluate and compare two different model designs for a specific prediction problem encountered in the WITAS UAV project. The problem is to predict the movements of a vehicle travelling in a traffic network. The main difficulty is that uncertainty in predictions is very high, du to two factors: predictions have to be made on a relatively large time scale, and we have very little information about the specific vehicle in question. To counter uncertainty, as much use as possible must be made of knowledge about traffic in general, which puts emphasis on the knowledge representation aspect of the predictive model design.The two mode design we develop differ mainly in how they represent uncertainty: the first uses coarse, schema-based representation of likelihood, while the second, a Markov model, uses probability. Preliminary experiments indicate that the second design has better computational properties, but also some drawbacks: model construction is data intensive and the resulting models are somewhat opaque.

Licentiate Thesis.In series: Linköping Studies in Science and Technology. Thesis #938. Institutionen för datavetenskap. 108 pages. ISBN:91-7373-313-X.Note: Report code: LiU-Tek-Lic-2002:11. The format of the electronic version of this thesis differs slightly from the printed one: this is due mainly to font compatibility. The figures and body of the thesis are remaining unchanged.

The overall objective of the Wallenberg Laboratory for Information Technology and Autonomous Systems (WITAS) at LinkÃ¶ping University is the development of an intelligent command and control system, containing vision sensors, which supports the operation of a unmanned air vehicle (UAV) in both semi- and full-autonomy modes. One of the UAV platforms of choice is the APID-MK3 unmanned helicopter, by Scandicraft Systems AB. The intended operational environment is over widely varying geographical terrain with traffic networks and vehicle interaction of variable complexity, speed, and density.The present version of APID-MK3 is capable of autonomous take-off, landing, and hovering as well as of autonomously executing pre-defined, point-to-point flight where the latter is executed at low-speed. This is enough for performing missions like site mapping and surveillance, and communications, but for the above mentioned operational environment higher speeds are desired. In this context, the goal of this thesis is to explore the possibilities for achieving stable ââaggressiveââ manoeuvrability at high-speeds, and test a variety of control solutions in the APID-MK3 simulation environment.The objective of achieving ââaggressiveââ manoeuvrability concerns the design of attitude/velocity/position controllers which act on much larger ranges of the body attitude angles, by utilizing the full range of the rotor attitude angles. In this context, a flight controller should achieve tracking of curvilinear trajectories at relatively high speeds in a robust, w.r.t. external disturbances, manner. Take-off and landing are not considered here since APIDMK3 has already have dedicated control modules that realize these flight modes.With this goal in mind, we present the design of two different types of flight controllers: a fuzzy controller and a gradient descent method based controller. Common to both are model based design, the use of nonlinear control approaches, and an inner- and outer-loop control scheme. The performance of these controllers is tested in simulation using the nonlinear model of APID-MK3.

This volume addresses all current aspects of relational methods and their applications in computer science. It presents a broad variety of fields and issues in which theories of relations provide conceptual or technical tools. The contributions address such subjects as relational methods in programming, relational constraints, relational methods in linguistics and spatial reasoning, relational modelling of uncertainty. All contributions provide the readers with new and original developments in the respective fields.The reader thus gets an interdisciplinary spectrum of the state of the art of relational methods and implementation-oriented solutions of problems related to these areas

As the scope of logics of action and change continues to increase and powerful research tools are developed, it becomes possible to model larger and more complex scenarios. Unfortunately the scenarios become harder to read and difficult to modify and debug with increasing size and complexity. These problems have been overlooked in the action and change community due to the fact that only smaller toy problems are considered. Sound modeling methodology is as essential as the primitives of the modeling language. The object-oriented paradigm is one structuring mechanism that alleviates these problems and provides a systematic means of scenario construction. The topic of this paper is to demonstrate how many ideas from the object orientation paradigm can be used when reasoning about action and change, we show this by integrating the technique directly in an existing logic of action and change without any modification to the underlying logical language or semantics. 1

The work reported in the paper is aimed at achieving aggressive manoeuvrability for an unmanned helicopter APID MK-III by Scandicraft AB in Sweden. The manoeuvrability problem is treated at the level of attitude (pitch, roll, yaw) and the aim is to achieve stabilization of the attitude angles within much larger ranges than currently available. We present a fuzzy gain scheduling control approach based on two different types of Iinearization of the original nonlinear APID MK-III model. The performance of the fuzzy gain scheduled controllers is evaluated in simulation and shows that they are effective means for achieving the desired robust manoeuvrability.

This work presents a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. We use a novel approach to the design consisting of two steps: 1) Mamdani-type of fuzzy rules to compute each of the desired horizontal velocity corresponding to the desired values for the attitude angles and the main rotor collective pitch; and 2) a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. The performance of the combined linguistic/model-based controller is evaluated in simulation and shows that the proposed design method achieves its intended purpose

In Proceedings of the IEEE International Symposium on Intelligent Control (CCA/ISIC), pages 348–352. ISBN:0-7803-6722-7.DOI:10.1109/ISIC.2001.971534.

The paper presents the design of a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. The controller is able of regulating high horizontal velocities via stabilization of the attitude angles within much larger ranges than currently available. We use a novel approach to the design consisting of two steps: 1) a Mamdani-type of a fuzzy rules are used to compute for each desired horizontal velocity the corresponding desired values for the attitude angles and the main rotor collective pitch; and 2) using a nonlinear model of the altitude and attitude dynamics, a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. According to our knowledge this is the first time when a combination of linguistic and model-based fuzzy control is used for the control of a complicated plant such as an autonomous helicopter. The performance of the combined linguistic/model-based controllers is evaluated in simulation and shows that the proposed design method achieves its intended purpose

In this paper we argue that RoboCup is a useful tool for the teaching of AI in undergraduate education. We provide case studies, from two Swedish universities, of how RoboCup based AI courses can be implemented using a problem based approach. Although the courses were successful there are significant areas for improvement. Firstly, to help students cope with the complexity of the domain we developed RoboSoc, a general software framework for developing simulated RoboCup agents. Secondly, we propose creating close co-operation between the teachers and researchers at Scandinavian Universities with the aim of increasing the motivation of both students and teachers by providing accessible information and competence.

In Proceedings of the IJCAI 2001 workshop on Planning under Uncertainty and Incomplete Information (PRO-2).

Prediction is found to be a part of many more complex reasoning problems, e.g. state estimation, planning and diagnosis. In spite of this, the prediction problem is rarely studied on its own. Yet there appears to be a wide range of choices for the design of the central component in a solution to this problem, the predictive model. We examine some of the alternatives and, as a case study, present two different solutions to a specific prediction problem that we have encountered in the WITAS UAV project.

We present an algorithm for planning with time and resources based on heuristic search. The algorithm minimizes makespan using an admissible heuristic derived automatically from the problem instance. Estimators for resource consumption are derived in the same way. The goals are twofold: to show the flexibility of the heuristic search approach to planning and to develop a planner that combines expressivity and performance. Two main issues are the definition of regression in a temporal setting and the definition of the heuristic estimating completion time. A number of experiments are presented for assessing the performance of the resulting planner.

In Proceedings of the 5th Symposium on Logical Formalizations of Commonsense Reasoning (CommonSense).

Although many formalisms for reasoning about action and change have been proposed in the literature, their semantic adequacy has primarily been tested using tiny domains that highlight some particular aspect or problem. However, since some of the classical problems are completely or partially solved and since powerful tools are available, it is now necessary to start modeling more complex domains. This paper presents a methodology for handling such domains in a systematic manner using an object-oriented framework and provides several examples of the elaboration tolerance exhibited by the resulting models.

TALPLANNER is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented, using,formulas in, a temporal logic called TAL, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALPLANNER exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition. In this article, we provide an overview of TALPLANNER.

The task of monitoring the execution of a software-based controller in order to detect, classify, and recover from discrepancies between the actual effects of control actions and the effects predicted by a model, is the topic of this thesis. Model-based execution monitoring is proposed as a technique for increasing the safety and optimality of operation of large and complex industrial process controllers, and of controllers operating in complex and unpredictable environments (such as unmanned aerial vehicles).In this thesis we study various aspects of model-based execution monitoring, including the following:The relation between previous approaches to execution monitoring in Control Theory, Artificial Intelligence and Computer Science is studied and a common conceptual framework for design and analysis is proposed.An existing execution monitoring paradigm, <em>ontological control</em>, is generalized and extended. We also present a prototype implementation of ontological control with a first set of experimental results where the prototype is applied to an actual industrial process control system: The ABB STRESSOMETER cold mill flatness control system.A second execution monitoring paradigm, <em>stability-based execution monitoring</em>, is introduced, inspired by the vast amount of work on the \"stability\" notion in Control Theory and Computer Science.Finally, the two paradigms are applied in two different frameworks. First, in the \"hybrid automata\" framework, which is a state-of-the-art formal modeling framework for hybrid (that is, discrete+continuous) systems, and secondly, in the logical framework of GOLOG and the Situation Calculus.

An autonomous agent operating in a dynamical environment must be able to perform several \"intelligent\" tasks, such as learning about the environment, planning its actions and reasoning about the effects of the chosen actions. For this purpose, it is vital that the agent has a coherent, expressive, and well understood means of representing its knowledge about the world.Traditionally, all knowledge about the dynamics of the modeled world has been represented in complex and detailed action descriptions. The first contribution of this thesis is the introduction of domain constraints in TAL, allowing a more modular representation of certain kinds of knowledge.The second contribution is a systematic method of modeling different types of conflict handling that can arise in the context of concurrent actions. A new type of fluent, called influence, is introduced as a carrier from cause to actual effect. Domain constraints govern how influences interact with ordinary fluents. Conflicts can be modeled in a number of different ways depending on the nature of the interaction.A fundamental property of many dynamical systems is that the effects of actions can occur with some delay. We discuss how delayed effects can be modeled in TAL using the mechanisms previously used for concurrent actions, and consider a range of possible interactions between the delayed effects of an action and later occurring actions.In order to model larger and more complex domains, a sound modeling methodology is essential. We demonstrate how many ideas from the object-oriented paradigm can be used when reasoning about action and change. These ideas are used both to construct a framework for high level control objects and to illustrate how complex domains can be modeled in an elaboration tolerant manner.

The goal of most agents is not just to reach a goal state, but rather also (or alternatively) to put restrictions on its trajectory, in terms of states it must avoid and goals that it must âmaintainâ. This is analogous to the notions of âsafetyâ and âstabilityâ in the discrete event systems and temporal logic community. In this paper we argue that the notion of âstabilityâ is too strong for formulating âmaintenanceâ goals of an agent â in particular, reactive and software agents, and give examples of such agents. We present a weaker notion of âmaintainabilityâ and show that our agents which do not satisfy the stability criteria, do satisfy the weaker criteria. We give algorithms to test maintainability, and also to generate control for maintainability. We then develop the notion of âsupportabilityâ that generalizes both âmaintainabilityâ and âstabilizability, develop an automata theory that distinguishes between exogenous and control actions, and develop a temporal logic based on it.

We argue that RoboCup can be used to improve the teaching of AI in undergraduate education. We give some examples of how AI courses using RoboCup can be implemented using a problem based approach at two different Universities. To reduce the negative aspects found we present a solution, with the aim of easing the burden of grasping the domain of RoboCup for the students, RoboSoc which is a general framework for developing simulated RoboCup agents.

Classical propositional STRIPS planning is nothing but the search for a path in the state transition graph induced by the operators in the planning problem. What makes the problem hard is the size and the sometimes adverse structure of this graph. We conjecture that the search for a plan would be more efficient if there were only a small number of paths from the initial state to the goal state. To verify this conjecture, we define the notion of reduced operator sets and describe ways of finding such reduced sets. We demonstrate that some state-of-the-art planners run faster using reduced operator sets.

In Steve Chien, Subbarao Kambhampati, Craig A. Knoblock, editors, Proceedings of the 5th International Conference on Artificial Intelligence Planning and Scheduling (AIPS), pages 140–149. AAAI Press. ISBN:978-1-57735-111-5.DOI:10.1609/aimag.v21i4.1536.Note: There is an error in the paper: the condition for commutativity of actions (section "Commutativity Pruning") must also include that neither action adds a precondition of the other. Thus, commutativity is not the same as Graphplan-style "non-interference".Link:http://swepub.kb.se/bib/swepub:oai:DiVA....

hsp and hspr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. hsp does a forward search from the initial state recomputing the heuristic in every state, while hspr does a regression search from the goal computing a suitable representation of the heuristic only once. Both planners have shown good performance, often producing solutions that are competitive in time and number of actions with the solutions found by Graphplan and sat planners. hsp and hsp r, however, are not optimal planners. This is because the heuristic function is not admissible and the search algorithms are not optimal. In this paper we address this problem. We formulate a new admissible heuristic for planning, use it to guide an ida search, and empirically evaluate the resulting optimal planner over a number of domains. The main contribution is the idea underlying the heuristic that yields not one but a whole family of polynomial and admissible heuristics that trade accuracy for efficiency. The formulation is general and sheds some light on the heuristics used in hsp and Graphplan, and their relation. It exploits the factored (Strips) representation of planning problems, mapping shortest-path problems in state-space into suitably defined shortest-path problems in atom-space. The formulation applies with little variation to sequential and parallel planning, and problems with different action costs.

Introduction The emphasis of FCFoo was mainly on building a library for developers of RoboCup teams, designed especially for educational use. After the competition the library was more or less totally rewritten and nally published as part of the Master Thesis of Fredrik Heintz [4]. The agents are built on a layered reactive-deliberative architecture. The four layers describes the agent on dierent levels of abstraction and deliberation. The lowest level is mainly reactive while the others are more deliberate. The teamwork is based on nite automatas and roles. A role is a set of attributes describing some of the behaviour of a player. The decision-making uses decisiontrees to classify the situation and select the appropriate skill to perform. The other two layers are used to calculate the actual command to be sent to the server. The agent architecture and the basic design are inspired by the champions of RoboCup'98, CMUnited [6, 7]. The idea of using decision-trees and role

One of the most widespread approaches to reactive planning is Schoppers' universal plans. We propose a stricter definition of universal plans which guarantees a weak notion of soundness, not present in the original definition, and isolate three different types of completeness that capture different behaviors exhibited by universal plans. We show that universal plans which run in polynomial time and are of polynomial size cannot satisfy even the weakest type of completeness unless the polynomial hierarchy collapses. By relaxing either the polynomial time or the polynomial space requirement, the construction of universal plans satisfying the strongest type of completeness becomes trivial. As an alternative approach, we study randomized universal planning. By considering a randomized version of completeness and a restricted (but nontrivial) class of problems, we show that there exists randomized universal plans running in polynomial time and using polynomial space which are sound and complete for the restricted class of problems. We also report experimental results on this approach to planning, showing that the performance of a randomized planner is not easily compared to that of a deterministic planner.

We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal and control formulas. The sequential version of TALplanner is presented. The expressivity of plan operators is then extended to deal with an interesting class of resource types. An algorithm for generating concurrent plans, where operators have varying durations and internal state, is also presented. All versions of TALplanner have been implemented. The potential of these techniques is demonstrated by applying TALplanner to a number of standard planning benchmarks in the literature.

Recently, a great deal of progress has been made using nonmonotonic temporal logics to formalize reasoning about action and change. In particular, much focus has been placed on the proper representation of non-deterministic actions and the indirect effects of actions. For the latter the use of causal or fluent dependency rule approaches has been dominant. Although much recent effort has also been spent applying the belief revision/update (BR/U) approach to the action and change domain, there has been less progress in dealing with nondeterministic update and indirect effects represented as integrity constraints. We demonstrate that much is to be gained by cross-fertilization between the two paradigms and we show this in the following manner. We first propose a generalization of the PMA, called the modified MPMA which uses intuitions from the TL paradigm to permit representation of nondeterministic update and the use of integrity constraints interpreted as causal or fluent dependency rules. We provide several syntactic characterizations of MPMA, one of which is in terms of a simple temporal logic and provide a representation theorem showing equivalence between the two. In constructing the MPMA, we discovered a syntactic anomaly which we call the redundant atom anomaly that many TL approaches suffer from. We provide a method for avoiding the problem which is equally applicable across paradigms. We also describe a syntactic characterization of MPMA in terms of Dijkstra semantics. We set up a framework for future generalization of the BR/U approach and conclude with a formal comparison of related approaches.

In the area of formal reasoning about action and change, one of the fundamental representation problems is providing concise modular and incremental specifications of action types and world models, where instantiations of action types are invoked by agents such as mobile robots. Provided the preconditions to the action are true, their invocation results in changes to the world model concomitant with the goal-directed behavior of the agent. One particularly difficult class of related problems, collectively called the qualification problem, deals with the need to find a concise incremental and modular means of characterizing the plethora of exceptional conditions that might qualify an action, but generally do not, without having to explicitly enumerate them in the preconditions to an action. We show how fluent dependency constraints together with the use of durational fluents can be used to deal with problems associated with action qualification using a temporal logic for action and change called TAL-Q. We demonstrate the approach using action scenarios that combine solutions to the frame, ramification, and qualification problems in the context of actions with duration, concurrent actions, nondeterministic actions, and the use of both Boolean and non-Boolean fluents. The circumscription policy used for the combined problems is reducible to the first-order case.

In Proceedings of the 14th European Conference on Artificial Intelligence (ECAI), pages 501–505. In series: Frontiers in Artificial Intelligence and Applications #54. IOS Press. ISBN:4274903885, 1586030132.Link:http://swepub.kb.se/bib/swepub:oai:DiVA....

We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as temporal formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal formula. We extend the sequential version of TALplanner, which has previously shown impressive performance on standard benchmarks, in two respects: 1) TALplanner is extended to generate concurrent plans, where operators have varied durations and internal state; and 2) the expressiveness of plan operators is extended for dealing with several different types of resources. The extensions to the planner have been implemented and concurrent planning with resources is demonstrated using an extended logistics benchmark.

We present a sound and complete, tractable inference method for reasoning with localized closed world assumptions (LCWAâs) which can be used in applications where a reasoning or planning agent can not assume complete information about planning or reasoning states. This <em>Open World Assumption</em> is generally necessary in most realistic robotics applications. The inference procedure subsumes that described in Etzioni et al [9], and others. In addition, it provides a great deal more expressivity, permitting limited use of negation and disjunction in the representation of LCWAâs, while still retaining tractability. The ap- proach is based on the use of circumscription and quantifier elimination techniques and inference is viewed as querying a deductive database. Both the preprocessing of the database using circumscription and quan- tifier elimination, and the inference method itself, have polynomial time and space complexity.

The purpose of this paper is to provide a broad overview of the WITAS Unmanned Aerial Vehicle Project. The WITAS UAV project is an ambitious, long-term basic research project with the goal of developing technologies and functionalities necessary for the successful deployment of a fully autonomous UAV operating over diverse geographical terrain containing road and traffic networks. Theproject is multi-disciplinary in nature, requiring many different research competences, and covering a broad spectrum of basic research issues, many of which relate to current topics in artificial intelligence. A number of topics considered are knowledge representation issues, active vision systems and their integration with deliberative/reactive architectures, helicopter modeling and control, ground operator dialogue systems, actual physical platforms, and a number of simulation techniques.

In Proceedings of the UAV 2000 International Technical Conference and Exhibition (UAV). Euro UVS.

The WITAS Unmanned Aerial Vehicle Project is a long term basic research project located at LinkÃ¶ping University (LIU), Sweden. The project is multi-disciplinary in nature and involves cooperation with different departments at LIU, and a number of other universities in Europe, the USA, and South America. In addition to academic cooperation, the project involves collaboration with a number of private companies supplying products and expertise related to simulation tools and models, and the hardware and sensory platforms used for actual flight experimentation with the UAV. Currently, the project is in its second phase with an intended duration from 2000-2003.This paper will begin with a brief overview of the project, but will focus primarily on the computer vision related issues associated with interpreting the operational environment which consists of traffic and road networks and vehicular patterns associated with these networks.

In this paper we present TAL-C, a logic of action and change for worlds with action concurrency. TAL-C has a first-order semantics and proof theory. It builds on an existing logic TAL, which includes the use of dependency laws for dealing with ramification. It is demonstrated how TAL-C can represent a number of phenomena related to action concurrency: action duration, how the effects of one action interferes with or enables another action, synergistic effects of concurrent actions, conflicting and cumulative effect interactions, and resource conflicts. A central idea is that actions are not described as having effects that directly alter the world state. Instead, actions produce influences, and the way these influences alter the world state are described in specialized influence laws. Finally, we address how TAL-C narratives can be written to support modularity.

While model checking algorithms are in theory efficient, they are in practice hampered by the explosive growth of system models. We show that for certain specifications the model cheking problem reduces to a question of reachability in the system state transition graph, and apply a simple, randomized algorithm to this problem.

Computer vision systems used in autonomous mobile vehicles are typically linked to higher-level deliberation processes. One important aspect of this link is how to connect, or anchor, the symbols used at the higher level to the objects in the vision system that these symbols refer to. Anchoring is complicated by the fact that the vision data are inherently affected by uncertainty. We propose an anchoring technique that uses fuzzy sets to represent the uncertainty in the perceptual data. We show examples where this technique allows a deliberative system to reason about the objects (cars) detected by a vision system embarked in an unmanned helicopter, in the framework of the Witas project.

Although many formalisms for reasoning about action exist, surprisingly few approaches have taken computational complexity into consideration. The contributions of this article are the following: a temporal logic with a restriction for which deciding satisfiability is tractable, a tractable extension for reasoning about action, and NP-completeness results for the unrestricted problems. Many interesting reasoning problems can be modelled, involving nondeterminism, concurrency and memory of actions. The reasoning process is proved to be sound and complete. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

Developing agents for simulation environments is usually the responsibility of computer experts. However, as domain experts have superior knowledge of the intended agent behavior, it is desirable to have domain experts directly specifying behavior. In this paper we describe a system which allows non-computer experts to specify the behavior of agents for the RoboCup domain. An agent designer is presented with a Graphical User Interface with which he can specify behaviors and activation conditions for behaviors in a layered behavior-based system. To support the testing and debugging process we are also developing interfaces that show, in real-time, the world from the agents perspective and the state of its reasoning process.

In this paper we present an AI programming organised around the RoboCup soccer simulation system. The course participants create a number of software agents that form a team, and participate in a tournament at the end of the course. The use of a challenging and interesting task, and the incentive of having a tournament has made the course quite successful, both in term of enthusiasm of the students and of knowledge acquired. In the paper we describe the structure of the course, discuss in what respect we think the course has met its aim, and the opinions of the students about the course.

In Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming (CP), pages 118–128. In series: Lecture Notes in Computer Science #1713. Springer. DOI:10.1007/978-3-540-48085-3_9.

The class of constraint satisfaction problems (CSPs) over finite domains has been shown to be NP-complete, but many tractable subclasses have been identified in the literature. In this paper we are interested in restrictions on the types of constraint relations in CSP instances. By a result of Jeavons et al. we know that a key to the complexity of classes arising from such restrictions is the closure properties of the sets of relations. It has been shown that sets of relations that are closed under constant, majority, affine, or associative, commutative, and idempotent (ACI) functions yield tractable subclasses of CSP. However, it has been unknown whether other closure properties may generate tractable subclasses. In this paper we introduce a class of tractable (in fact, SL-complete) CSPs based on bipartite graphs. We show that there are members of this class that are not closed under constant, majority, affine, or ACI functions, and that it, therefore, is incomparable with previously identified classes.

In Proceedings of the 5th European Conference on Planning (ECP), pages 308–318. In series: Lecture Notes in Computer Science #1809. Springer. DOI:10.1007/10720246_24.

Planning with incomplete information may mean a number of different things, that certain facts of the initial state are not known, that operators can have random or nondeterministic effects, or that the plans created contain sensing operations and are branching. Study of the complexity of incomplete information planning has so far been concentrated on probabilistic domains, where a number of results have been found. We examine the complexity of planning in nondeterministic propositional domains. This differs from domains involving randomness, which has been well studied, in that for a nondeterministic choice, not even a probability distribution over the possible outcomes is known. The main result of this paper is that the non-branching plan existence problem in unobservable domains with an expressive operator formalism is EXPSPACE-complete. We also discuss several restrictions, which bring the complexity of the problem down to PSPACF-complete, and extensions to the fully and partially observable cases.

We introduce the notion of a meta-query on relational databases and a technique which can be used to represent and solve a number of interesting problems from the area of knowledge representation using logic. The technique is based on the use of quantifier elimination and may also be used to query relational databases using a declarative query language called SHQL (Semi-Horn Query Language), introduced in [6]. SHQL is a fragment of classical first-order predicate logic and allows us to define a query without supplying its explicit definition. All SHQL queries to the database can be processed in polynomial time (both on the size of the input query and the size of the database). We demonstrate the use of the technique in problem solving by structuring logical puzzles from the Knights and Knaves domain as SHQL meta-queries on relational databases. We also provide additional examples demonstrating the flexibility of the technique. We conclude with a description of a newly developed software tool, The Logic Engineer, which aids in the description of algorithms using transformation and reduction techniques such as those applied in the meta-querying approach.

In this paper, we consider the problem of expressing and computing queries on relational deductive databases in a purely declarative query language, called SHQL (Semi-Horn Query Language). Assuming the relational databases in question are ordered, we show that all SHQL queries are computable in PTIME (polynomial time) and the whole class of PTIME queries is expressible in SHQL. Although similar results have been proven for fixpoint languages and extensions to datalog, the claim is that SHQL has the advantage of being purely declarative, where the negation operator is interpreted as classical negation, mixed quantifiers may be used and a query is simply a restricted first-order theory not limited by the rule-based syntactic restrictions associated with logic programs in general. We describe the PTIME algorithm used to compute queries in SHQL which is based in part on quantifier elimination techniques and also consider extending the method to incomplete relational databases using intuitions related to circumscription techniques.

We present a new forward chaining planner, TALplanner, based on ideas developed by Bacchus and Kabanza, where domain-dependent search control knowledge represented as temporal formulas is used to effectively control forward chaining. Instead of using a linear modal tense logic as with Bacchus and Kabanza, we use TAL, a narrative-based linear temporal logic used for reasoning about action and change in incompletely specified dynamic environments. Two versions of TALplanner are considered, TALplan/modal which is based on the use of emulated modal formulas and a progression algorithm, and TALplan/non-modal which uses neither modal formulas nor a progression algorithm. For both versions of TALplanner and for all tested domains, TALplanner is shown to be considerably faster and requires less memory. The TAL versions also permit the representation of durative actions with internal state.

In this paper, we propose a new way of considering reasoning about action and change. Rather than placing a preferential structure onto the models of logical theories, we place such a structure directly on the semantics of the actions involved. In this way, we obtain a preferential semantics of actions by means of which we can not only deal with several of the traditional problems in this area such as the frame and ramification problems, but can generalize these solutions to a context which includes both nondeterministic and concurrent actions. In fact, the net result is an integration of semantical and verificational techniques from the paradigm of imperative and concurrent programs in particular, as known from traditional programming, with the AI perspective. In this paper, the main focus is on semantical (i.e. model theoretical) issues rather than providing a logical calculus, which would be the next step in the endeavor.

The area of reasoning about action and change is concerned with the formalization of actions and their effects as well as other aspects of inhabited dynamical systems. The representation is typically done in some logical language. Although there has been substantial progress recently regarding the frame problem and the ramification problem, many problems still remain. One of these problems is the representation of concurrent actions and their effects. In particular, the effects of two or more actions executed concurrently may be different from the union of the effects of the individual actions had they been executed in isolation. This thesis presents a language, TAL-C, which supports detailed and flexible yet modular descriptions of concurrent interactions. Two related topics, which both require a solution to the concurrency problem, are also addressed: the representation of effects of actions that occur with some delay, and the representation of actions that are caused by other actions.Another aspect of reasoning about action and change is how to describe higher-level reasoning tasks such as planning and explanation. In such cases, it is important not to just be able to reason about a specific narrative (course of action), but to reason about alternative narratives and their properties, to compare and manipulate narratives, and to reason about alternative results of a specific narrative. This subject is addressed in the context of the situation calculus, where it is shown how the standard version provides insufficient support for reasoning about alternative results, and an alternative version is proposed. The narrative logic NL is also presented; it is based on the temporal action logic TAL, where narratives are represented as first-order terms. NL supports reasoning about (I) metric time, (II) alternative ways the world can develop relative to a specific choice of actions, and (III) alternative choices of actions.

Intelligent agents embedded in physical environments need the ability to connect, or <em>anchor</em>, the symbols used to perform abstract reasoning to the physical entities which these symbols refer to. Anchoring must deal with indexical and objective references, definite and indefinite identifiers, and a temporary impossibility to perceive physical entities. Furthermore it needs to rely on sensor data which is inherently affected by uncertainty, and to deal with ambiguities. In this thesis, we outline the concept of anchoring and its functionalities. Moreover we define the general structure for an anchoring module and we present an implementation of the anchoring functionalities in two different domains: an autonomous airborne vehicle for traffic surveillance and a mobile ground vehicle performing navigation.

Silvia Coradeschi, Lars Karlsson and Klas Nordberg.
1999.Integration of vision and decision-making in an autonomous airborne vehicle for traffic surveillance.

In Proceedings of the International Conference on Vision Systems '99: Grand Canary.

In this paper we present a system which integrates computer vision and decision-making in an autonomous airborne vehicle that performs traffic surveillance tasks. The main factors that make the integration of vision and decision-making a challenging problem are: the qualitatively different kind of information at the decision-making and vision levels, the need for integration of dynamically acquired information with a priori knowledge, e.g. GIS information, and the need of close feedback and guidance of the vision module by the decision-making module. Given the complex interaction between the vision module and the decision-making module we propose the adoption of an intermediate structure, called Scene Information Manager, and describe its structure and functionalities.

The autonomy of an artificial agent (e.g. a robot) will certainly depend on its ability to perform \"intelligent\" tasks, such as learning, planning, and reasoning about its own actions and their effects on the enviroment, for example predicting the consequences of its own behaviour. To be able to perform these (and many more) tasks, the agent will have to represent its knowledge about the world.The research field \"Logics of Action Change\" is concerned with the modelling of agents and dynamical, changing environments with logics.In this thesis we study two aspects of automation of logics of action and change. The first aspect, regression, is used to \"reason backwards\", i.e. to start with the last time point in a description of a course of events, and moving backwards through these events, taking the effects of all actions into consideration. We discuss the consequences for regression of introducing nondeterministic actions, and provide the logic PMON with pre- and postdiction procedures. We employ the classical computer science tool, the weakest liberal precondition operator (wlp) for this, and show that logical entailment of PMON is equivalent to wlp computations.The second aspect is computational complexity of logics of action and change, which has virtually been neglected by the research community. We present a new and expressive logic, capable of expressing continuous time, nondeterministic actions, concurrency, and memory of actions. We show that satisfiability of a theory in this logic is NP-complete. Furthermore, we identify a tractable subset of the logic, and provide a sound, complete, and polynomial algorithm for satisfiability of the subset.

<strong></strong> We propose an approach to modeling delayed effects of actions which is based on the use of causal constraints and their interaction with the direct effects of actions. The approach extends previous work with a causal approach used to deal with the ramification problem. We show the similarity between solutions to the modeling of indirect effects and delayed effects of actions by example. The base logic PMON+ is a temporal logic for reasoning about action and change and uses circumscription. It is shown that the extension for delayed effects of actions retains the first-order reducibility property shown previously for successfully dealing with the frame and ramification problems for a large class of action scenarios. We also consider the âcausal qualificationâ problem, \"natural death\" of fluents and causal lag, each of which is closely related to the use of delayed effects.

The purpose of this article is to provide a uniform, lightweight language specication and tutorial for a class of temporal logics for reasoning about action and change that has been developed by our group during the period 1994-1998. The class of logics are collected under the name TAL, an acronym for Temporal Action Logics. TAL has its origins and inspiration in the work with Features and Fluents (FF) by Sandewall, but has diverged from the methodology and approach through the years. We first discuss distinctions and compatibility with FF, move on to the lightweight language specication, and then present a tutorial in terms of an excursion through the different parts of a relatively complex narrative defined using TAL. We conclude with an annotated list of published work from our group. The article tries to strike a reasonable balance between detail and readability, making a number of simplications regarding narrative syntax and translation to a base logical language. Full details are available in numerous technical reports and articles which are listed in the final section of this article.

We first define general domain circumscription (GDC) and provide it with a semantics. GDC subsumes existing domain circumscription proposals in that it allows varying of arbitrary predicates, functions, or constants, to maximize the minimization of the domain of a theory. We then show that for the class of semi-universal theories without function symbols, that the domain circumscription of such theories can be constructively reduced to logically equivalent first-order theories by using an extension of the DLS algorithm, previously proposed by the authors for reducing second-order formulas. We also show that for a certain class of domain circumscribed theories, that any arbitrary second-order circumscription policy applied to these theories is guaranteed to be reducible to a logically equivalent first-order theory. In the case of semi-universal theories with functions and arbitrary theories which are not separated, we provide additional results, which although not guaranteed to provide reductions in all cases, do provide reductions in some cases. These results are based on the use of fixpoint reductions.

In Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 258–269. Morgan Kaufmann Publishers.

Using intuitions from the temporal reasoning community, we provide a generalization of the PMA, called the modified PMA (MPMA), which permits the representation of disjunctive updates and the use of integrity constraints interpreted as causal constraints. In addition, we provide a number of syntactic characterizations of the MPMA, one of which is constructed by mapping an MPMA update of a knowledge base into a temporal narrative in a simple temporal logic (STL). The resulting representation theorem provides a basis for computing entailments of the MPMA and could serve as a basis for further generalization of the belief update approach for reasoning about action and change.

The use of causal rules or fluent dependency constraints has proven to provide a versatile means of dealing with the ramification problem. In this paper we show how fluent dependency constraints together with the use of durational fluents can be used to deal with problems associated with action qualification. We provide both a \emph{weak} and \emph{strong} form of qualification and demonstrate the approach using an action scenario which combines solutions to the frame, ramification and qualification problems in the context of actions with duration, concurrent actions, non-deterministic actions and the use of both boolean and non-boolean fluents. The circumscription policy used for the combined problems is reducible to the 1st-order case. In addition, we demonstrate the use of a research tool VITAL, for querying and visualizing action scenarios.

The international journal of high performance computing applications, 11(4):299–313. Sage Publications. DOI:10.1177/109434209701100404.

Rolling bearing simulations are very computationally in tensive. Serial simulations may take weeks to execute, and there is a need to use the potential of parallel comput ing. The specific structure of the rolling bearing problem is used to develop suitable scheduling strategies. The authors discuss the system of stiff ordinary differential equations arising from a bearing model and show how to numerically solve these ordinary differential equations on parallel computers. Benchmarking results are presented for two test cases on three platforms.

LinkÃ¶ping University has recently created a separate entity, called <em>LinkÃ¶ping University Electronic Press</em>, for the <em>unrefereed electronic publication</em> of research articles and other university-related materials over the Internet. The present article presents the background for why the E-Press was created and the strategies which have been chosen for its operation at least during its initial period. The article identifies three key problems in the context of this strategy: ï»¿<ul><li> The purely <em>formal problems</em> concerning what counts as a publication; </li><li> The <em>persistence problem</em> of making sure that an electronically published article does not change over time; </li><li> The <em>reception problems</em> concerning how fellow researchersand the academic community regard electronically published articles. </li></ul>We describe how the formal problems and the persistence problems have been addressed in the E-Press initiative. With respect to the reception problems, we argue that scientific journals and journal-like conferences presently perform four distinct functions, and that these functions can be performed better if they are âunbundledâ and addressed by other means. The four functions are:<ul><li> Publication in the narrow sense - making the article publicly available; </li><li> Scientific quality control through reviewing; </li><li> Selection of relevant articles for the benefit of the researcher-reader; </li><li> Promotion of the scientific results of the author. </li></ul>The Electronic Press focusses on the first one of these four functions. We discuss how the other three functions can be separated and performed by other means than through a conventional journal or quality conference proceedings.<ul></ul><ul></ul>

I propose a <em>neo-classical</em> structure for publishing and reviewing of scientific works. This proposal has the following characteristic components:<ul><li> Electronic âpreprint archivesâ and other similar mechanisms where research articles are made publicly available without prior formal review are considered as true and full-fledged <em>publication</em> of research from the point of view of priority of results. </li><li> Large parts of the reviewing process is done publicly and in the form of published review letters and other contributions to the scientific debate, rather than through anonymous and confidential review statements which dominate today. There is a switch from anonymous âpass-failâ reviewing towards <em>open reviewing</em> where the identity and the comments of the reviewers are made public. </li><li> Since open reviewing happens <em>after</em> publication, rather than before, there is a second step where articles are promoted to ârecommendedâ or âcertifiedâ status through the decision of a review committee. The requirements for certification are set at least as high as for the formally published journal articles of today, so that it counts like journal publication in a CV. </li><li> Several techniques are foreseen for facilitating the selection process of the individual reader as well as for improving communication as such between researchers. </li><li> One should accept that there are good reasons why there may be several articles (from the same author) presenting the same result. This suggests the introduction of a formal concept of a âresultâ which is represented by several publications, and to allow citations to refer to results rather than to some specific publication of the result. </li></ul>I refer to this system as <em>neo-classical</em> because it assumes that peer review is done <em>openly</em> and <em>after</em> an article has been published. It is of course only proposed as a complement which can easily co-exist with the modern system, allowing each author to choose which of the two systems he or she wishes to use for a particular article.<ul></ul>

In recent years, a great deal of attention has been devoted to logics of common-sense reasoning. Among the candidates proposed, circumscription has been perceived as an elegant mathematical technique for modeling nonmonotonic reasoning, but difficult to apply in practice. The major reason for this is the second-order nature of circumscription axioms and the difficulty in finding proper substitutions of predicate expressions for predicate variables. One solution to this problem is to compile, where possible, second-order formulas into equivalent first-order formulas. Although some progress has been made using this approach, the results are not as strong as one might desire and they are isolated in nature. In this article, we provide a general method that can be used in an algorithmic manner to reduce certain circumscription axioms to first-order formulas. The algorithm takes as input an arbitrary second-order formula and either returns as output an equivalent first-order formula, or terminates with failure. The class of second-order formulas, and analogously the class of circumscriptive theories that can be reduced, provably subsumes those covered by existing results. We demonstrate the generality of the algorithm using circumscriptive theories with mixed quantifiers (some involving Skolemization), variable constants, nonseparated formulas, and formulas with n-ary predicate variables. In addition, we analyze the strength of the algorithm, compare it with existing approaches, and provide formal subsumption results.

CARABAS (Coherent All RAdio BAnd Sensing) is a new type of radar that has the unique property of being able to penetrate through vegetation, and to some extent into upper levels of soil depending on water content. This can be done by using long radar waves in the range 3â15 meters, and new algorithms for image reconstruction from information in reflected radar waves. These algorithms are related to methods used for computer tomography, and are very computationally expensive. Two classes of algorithms for image reconstruction are direct Fourier methods and filtered backprojection. Even though filtered backprojection is more computationally demanding, we chose that method since it is easier to parallelize, it has better real-time properties, and it is easier to compensate for disturbances and achieve good image quality.In this paper we report results from the first parallel implementation of the CARABAS algorithms. The benchmarking was done on a Parsytec PowerGC MIMD computer with 128 PowerPC 601 processors. We come close to achieving the real-time requirement for significant parts of the computation.

In the current paper we present a powerful technique of obtaining natural deduction proof systems for first-order fixpoint logics. The term fixpoint logics refers collectively to a class of logics consisting of modal logics with modalities definable at meta-level by fixpoint equations on formulas. The class was found very interesting as it contains most logics of programs with e.g. dynamic logic, temporal logic and the Â¯-calculus among them. In this paper we present a technique that allows us to derive automatically natural deduction systems for modal logics from fixpoint equations defining the modalities

This report describes the current state of work with PMON, a logic for reasoning about action and change, and its extensions. PMON has been assessed correct for the K-IA class using Sandewall's Features and Fluents framework which provides tools for assessing the correctness of logics of action and change. A syntactic characterization of PMON has previously been provided in terms of a circumscription axiom which is shown to be reducible to a first-order formula. This report introduces a number of new extensions which are also reducible and deal with ramification. The report is intended to provide a formal specification for the PMON family of logics and the surface language L(SD) used to represent action scenario descriptions. It should be considered a working draft. The title of the report has a version number because both the languages and logics used are continually evolving. Since this document is intended as a formal specification which is used by our group as a reference for research and implementation, it is understandably brief as regards intuitions and applications of the languages and logics defined. We do provide a set of benchmarks and comments concerning these which can serve as a means of comparing this formalism with others. The set of benchmarks is not complete and is only intended to provide representative examples of the expressivity and use of this particular family of logics. We describe its features and limitations in other publications by our group which can normally be found at \"http://www.ida.liu.se/labs/kplab/\".

In this paper, we propose a new way of considering reasoning about action and change. Rather than placing a preferential structure onto the models of logical theories, we place such a structure directly on the semantics of the actions involved. In this way, we obtain a preferential semantics of actions by means of which we can not only deal with several of the traditional problems in this area such as the frame and ramification problems, but can generalize these solutions to a context which includes both nondeterministic and concurrent actions. In fact, the net result is an integration of semantical and verificational techniques from the paradigm of imperative and concurrent programs in particular, as known from traditional programming, with the AI perspective. In this paper, the main focus is on semantical (i.e. model theoretical) issues rather than providing a logical calculus, which would be the next step in the endeavor.

Circumscription has been perceived as an elegant mathematical technique for modeling nonmonotonic and commonsense reasoning, but difficult to apply in practice due to the use of second-order formulas. One proposal for dealing with the computational problems is to identify classes of first-order formulas whose circumscription can be shown to be equivalent to a first-order formula. In previous work, we presented an algorithm which reduces certain classes of second-order circumscription axioms to logically equivalent first-order formulas. The basis for the algorithm is an elimination lemma due to Ackermann. In this paper, we capitalize on the use of a generalization of Ackermann's Lemma in order to deal with a subclass of universal formulas called <em>semi-Horn formulas</em>. Our results subsume previous results by Kolaitis and Papadimitriou regarding a characterization of circumscribed definite logic programs which are first-order expressible. The method for distinguishing which formulas are reducible is based on a boundedness criterion. The approach we use is to first reduce a circumscribed semi-Horn formula to a fixpoint formula which is reducible if the formula is bounded, otherwise not. In addition to a number of other extensions, we also present a fixpoint calculus which is shown to be sound and complete for bounded fixpoint formulas.

In this paper, we extend PMON, a logic for reasoning about action and change, with causal rules which are used to specify the indirect effects of actions The extension, called PMON(RCs), has the advantage of using explicit time, includes actions with durations, nondeterministic actions, allows partial specification of the timing and order of actions and has been assessed correct for at least the K-IA class of action scenarios within the Features and Fluents framework Most importantly, the circumscription policy used is easily shown to be reducible to the firstorder case which insures that standard theorem proving techniques and their optimizations may be used to compute entailment In addition, we show how the occlusion concept previously used to deal with duration and nondeterministic actions proves to be equally versatile in representing causal constraints and delayed effects of actions We also discuss related work and consider the strong correspondence between our work and recent work by Lin, who uses a Cause predicate to specify indirect effects similar to our use of Occlude in PMON, and a minimization policy related to that used in PMON.

Recently, Haas, Schubert, and Reiter, have developed an alternative approach to the frame problem which is based on the idea of using <em>explanation closure axioms</em>. The claim is that there is a monotonic solution for characterizing nonchange in serial worlds with fully specified actions, where one can have both a succinct representation of frame axioms and an effective proof theory for the characterization. In the paper, we propose a circumscriptive version of explanation closure, PMON, that has an effective proof theory and works for both context dependent and nondeterministic actions. The approach retains representational succinctness and a large degree of elaboration tolerance, since the process of generating closure axioms is fully automated and is of no concern to the knowledge engineer. In addition, we argue that the monotonic/nonmonotonic dichotomy proposed by others is not as sharp as previously claimed and is not fully justified.

We first define general domain circumscription (GDC) and provide it with a semantics. GDC subsumes existing domain circumscription proposals in that it allows varying of arbitrary predicates, functions, or constants, to maximize the minimization of the domain of a theory We then show that for the class of semi-universal theories without function symbols, that the domain circumscription of such theories can be constructively reduced to logically equivalent first-order theories by using an extension of the DLS algorithm, previously proposed by the authors for reducing second-order formulas. We also isolate a class of domain circumscribed theories, such that any arbitrary second-order circumscription policy applied to these theories is guaranteed to be reducible to a logically equivalent first-order theory. In the case of semi-universal theories with functions and arbitrary theories which are not separated, we provide additional results, which although not guaranteed to provide reductions in all cases, do provide reductions in some cases. These results are based on the use of fixpoint reductions.

Time and logic are central driving concepts in science and technology. In this book, some of the major current developments in our understanding and application of temporal logic are presented in computational terms. \"Time and Logic: A Computational Approach\" should be a useful sourcebook for those within the specific field of temporal logic, as well as providing valuable introductory material for those seeking an entry into this increasingly important area of theoretical computing.; The emphasis of the book is on presenting a broad range of approaches to computational applications. The techniques used will also be applicable in many cases to formalize beyond temporal logic alone, and it is hoped that adaptation to many different logics of programmes will be facilitated. Throughout, the authors have kept implementation-oriented solutions in mind.; The book begins with an introduction to the basic ideas of temporal logic. Successive chapters then examine particular aspects of the temporal theoretical computing domain, relating their applications to familiar areas of research, such as stochastic process theory, automata theory, established proof systems, model checking, relational logic and classical predicate logic. This should be a useful addition to the library of all theoretical computer scientists, providing a synthesis of well established results in temporal logic with the most up-to-date findings of some of the world's leading theoreticians.

In this paper we study correspondences between modal proof rules and the classical logic. The method we apply is based on an Ackermann's technique of eliminating second-order quantifiers from formulas. We show that the process of finding suitable correspondences can be reduced to a few simple steps. Moreover, the whole technique can be fully mechanized. We thus provide the reader with a powerful tool, useful in automated translations between modal logics and the classical logic.

In recent years, a great deal of attention has been devoted to logics of \"commonsense\" reasoning. Among the candidates proposed, circumscription has been perceived as an elegant mathematical technique for modeling nonmonotonic reasoning, but difficult to apply in practice. The major reason for this is the nd-order nature of circumscription axioms and the difficulty in finding proper substitutions of predicate expressions for predicate variables. One solution to this problem is to compile, where possible, nd-order formulas into equivalent 1st-order formulas. Although some progress has been made using this approach, the results are not as strong as one might desire and they are isolated in nature. In this article, we provide a general method which can be used in an algorithmic manner to reduce circumscription axioms to 1st-order formulas. The algorithm takes as input an arbitrary 2nd-order formula and either returns as output an equivalent 1st-order formula, or terminates with failure. The class of 2nd-order formulas, and analogously the class of circumscriptive theories which can be reduced, provably subsumes those covered by existing results. We demonstrate the generality of the algorithm using circumscriptive theories with mixed quantifiers (some involving Skolemization), variable constants, non-separated formulas, and formulas with n-ary predicate variables. In addition, we analyze the strength of the algorithm and compare it with existing approaches providing formal subsumption results.

Sandewall has recently proposed a systematic approach to the representation of knowledge about dynamical systems that includes a general framework in which to assess the range of applicability of existing and new logics for action and change and to provide a means of studying whether and in what sense the logics of action and change are relevant for intelligent agents. As part of the framework, a number of logics of preferential entailment are introduced and assessed for particular classes of action scenario descriptions. This paper provides syntactic characterizations of several of these relations of preferential entailment in terms of standard FOPC and circumscription axioms. The intent is to simplify the process of comparison with existing formalisms which use more traditional techniques and to provide a basis for studying the feasibility of compiling particular classes of problems into logic programs.

The current paper is devoted to automated techniques in the correspondence theory. The theory we deal with concerns the problem of finding classical first-order axioms corresponding to propositional modal schemas. Given a modal schema and a semantics base method of translating propositional modal formulae into classical first-order ones, we try to derive automatically classica first-order formulae characterizing precisely the class of frames validating the schema. The technique we consider can, in many cases, be easily applied even without computer support. Although we mainly concentrate on Kripke semantics, the technique we apply is much more general, as it is based on elimination of second-order quantifiers from formulae. We show many examples of application of the method. These can also serve as new, automated proofs of considered correspondences.

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 1(2):167–182. World Scientific. DOI:10.1142/S0218488593000097.

We consider the possibility of generalizing the notion of a fuzzy If-Then rule to take into account its context dependent nature. We interpret fuzzy rules as modeling a forward directed causal relationship between the antecedent and the conclusion, which applies in most contexts, but on occasion breaks down in exceptional contexts. The default nature of the rule is modeled by augmenting the original If-Then rule with an exception part. We then consider the proper semantic correlate to such an addition and propose a ternary relation which satisfies a number of intuitive constraints described in terms of a number of inference rules. In the rest of the paper, we consider implementational issues arising from the unless extension and propose the use of reason maintenance systems, in particular TMS's, where a fuzzy If-Then-Unless rule is encoded into a dependency net. We verify that the net satisfies the constraints stated in the inference schemes and conclude with a discussion concerning the integration of qualitative IN-OUT labelings of the TMS with quantitative degree of membership labelings for the variables in question.

In Proceedings of the 22nd International Symposium on Multiple-Valued Logic (SMVL), pages 146–154. In series: Proceedings of the International Symposium on Multiple Valued Logic #??. IEEE Computer Society. ISBN:0-8186-2680-1.

A nonmonotonic logic with explicit defaults, NML3, is presented. It is characterized by the following features: (1) the use of the strong Kleene three-valued logic as a basis; (2) the addition of an explicit default operator which enables distinguishing tentative conclusions from ordinary conclusions in the object language; and (3) the use of the idea of preferential entailment to generate nonmonotonic behavior. The central feature of the formalism, the use of an explicit default operator with a model-theoretic semantics based on the notion of a partial interpretation, distinguishes NML3 from most previous formalisms. By capitalizing on the distinction between tentative and ordinary conclusions, NML3 provides increased expressibility in comparison to many of the standard nonmonotonic formalisms and greater flexibility in the representation of subtle aspects of default reasoning. This is shown through examples.

We introduce and discuss a notion of strictly arithmetical completeness related to relative completeness of Cook (1978) and arithmetical completeness of Harel (1978). We present a powerful technique of obtaining strictly arithmetical axiomatizations of logics of programs. Given a model-theoretic semantics of a logic and a set of formulae defining (in a metalanguage) its nonclassical connectives, we automatically derive strictly arithmetically complete and sound proof systems for this logic. As examples of application of the technique we obtain new axiomatizations of algorithmic logic, (concurrent) dynamic logic and temporal logic.

The thesis is a study of a particular approach to defeasible reasoning based on the notion of an information state consisting of a set of partial interpretations constrained by an information ordering. The formalism proposed, called NML3, is a non-monotonic logic with explicit defaults and is characterized by the following features: (1) The use of the strong Kleene three-valued logic as a basis. (2) The addition of an explicit default operator which enables distinguishing tentative conclusions from ordinary conclusions in the object language. (3) The use of the technique of preferential entailment to generate non-monotonic behavior. The central feature of the formalism, the use of an explicit default operator with a model theoretic semantics based on the notion of a partial interpretation, distinguishes NML3 from the existing formalisms. By capitalizing on the distinction between tentative and ordinary conclusions, NML3 provides increased expressibility in comparison to many of the standard non-monotonic formalisms and greater flexibility in the representation of subtle aspects of default reasoning.In addition to NML3, a novel extension of the tableau-based proof technique is presented where a signed formula is tagged with a set of truth values rather than a single truth value. This is useful if the tableau-based proof technique is to be generalized to apply to the class of multi-valued logics. A refutation proof procedure may then be used to check logical consequence for the base logic used in NML3 and to provide a decision procedure for the propositional case of NML3.A survey of a number of non-standard logics used in knowledge representation is also provided. Various formalisms are analyzed in terms of persistence properties of formulas and their use of information structures.

The subject of this thesis is the formalization of a type of non-monotonic reasoning using a three-valued logic based on the strong definitions of Kleene. Non-monotonic reasoning is the rule rather than the exception when agents, human or machine, must act where information about the environment is uncertain or incomplete. Information about the environment is subject to change due to external causes, or may simply become outdated. This implies that inferences previously made may no longer hold and in turn must be retracted along with the revision of other information dependent on the retractions. This is the variety of reasoning we would like to find formal models for.We start by extending Kleene-s three-valued logic with an \"external negation\" connective where ~ a is true when a is false or unknown. In addition, a default operator D is added where D a is interpreted as \"a is true by default. The addition of the default operator increases the expressivity of the language, where statements such as \"a is not a default\" are directly representable. The logic has an intuitive model theoretic semantics without any appeal to the use of a fixpoint semantics for the default operator. The semantics is based on the notion of preferential entailment, where a set of sentences G preferentially entails a sentence a, if and only if a preferred set of the models of G are models of a. We also show that one version of the logic belongs to the class of cumulative non-monotonic formalisms which are a subject of current interest.A decision procedure for the propositional case, based on the semantic tableaux proof method is described and serves as a basis for a QA-system where it can be determined if a sentence a is preferentially entailed by a set of premises G. The procedure is implemented.

In this paper, we propose a formalization of non-monotonic reasoning using a three-valued logic based on the strong definitions of Kleene. We start by extending Kleene's three-valued logic with an \"external negation\" connective where ~ alpha is true when alpha is false or unknown. In addition, a default operator D is added where D alpha is interpreted as \"alpha is true by default\". The addition of the default operator increases the expressivity of the language, where statements such as \"alpha is not a default\" are directly representable. The logic has an intuitive model theoretic semantics without any appeal to the use of a fixpoint semantics for the default operator. The semantics is based on the notion of preferential entailment, where a set of sentences Gamma preferentially entails a sentence alpha, if and only if a preferred set of the models of Gamma are models of alpha. We also show that the logic belongs to the class of cumulative non-monotonic formalisms which are a subject of current interest.

Patrick Doherty.
1989.A correspondence between inheritance hierarchies and a logic of preferential entailment.

In M. L. Emrich, M. S. Pfeifer, M. Hadzikadic, and Z. W. Ras, editors, Proceedings of the 4th International Symposium on Methodologies for Intelligent Systems (ISMIS). University of North Carolina Press.

An alternative method of describing semantics of cause-effect structures is presented. It is based on a model of discrete dynamic systems. The model is similar to a condition-event Petri net, differing in the way restrictions on the alterability of actions are imposed.

The results presented in this paper concern the axiomatizability problem of first-order temporal logic with linear and discrete time. We show that the logic is incomplete, i.e., it cannot be provided with a finitistic and complete proof system. We show two incompleteness theorems. Although the first one is weaker (it assumes some first-order signature), we decided to present it, for its proof is much simpler and contains an interesting fact that finite sets are characterizable by means of temporal formulas. The second theorem shows that the logic is incomplete independently of any particular signature.

As shown in (Szalas, 1986, 1986, 1987) there is no finitistic and complete axiomatization of First-Order Temporal Logic of linear and discrete time. In this paper we give an infinitary proof system for the logic. We prove that the proof system is sound and complete. We also show that any syntactically consistent temporal theory has a model. As a corollary we obtain that the Downward Theorem of Skolem, Lowenheim and Tarski holds in the case of considered logic.

In this paper we consider the first-order temporal logic with linear and discrete time. We prove that the set of tautologies of this logic is not arithmetical (i.e., it is neither <em>Î£</em><sup>0</sup><sub><em>n</em></sub> nor <em>Î </em><sup>0</sup><sub><em>n</em></sub> for any natural number <em>n</em>). Thus we show that there is no finitistic and complete axiomatization of the considered logic.

Program manipulation is the task to perform transformations on program code, and is normally done in order to optimize the code with respect of the utilization of some computer resource. Partial evaluation is the task when partial computations can be performed in a program before it is actually executed. If a parameter to a procedure is constant a specialized version of that procedure can be generated if the constant is inserted instead of the parameter in the procedure body and as much computations in the code as possible are performed.A system is described which works on programs written in INTERLISP, and which performs partial evaluation together with other transformations such as beta-expansion and certain other optimization operations. The system works on full LISP and not only for a \"pure\" LISP dialect, and deals with problems occurring there involving side-effects, variable assignments etc. An analysis of a previous system, REDFUN, results in a list of problems, desired extensions and new features. This is used as a basis for a new design, resulting in a new implementation, REDFUN-2. This implementation, design considerations, constraints in the system, remaining problems, and other experience from the development and experiments with the system are reported in this paper.